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  • 251.
    Deka, Shankar
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Vaidya, Umesh
    Clemson, South Carolina, USA.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Clemson University.
    Navigation in Time-Varying Densities: An Operator Theoretic Approach2023In: 2023 European Control Conference, ECC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper (Refereed)
    Abstract [en]

    This paper considers the problem of optimizing robot navigation with respect to a time-varying objective encoded into a navigation density function. We are interested in designing state feedback control laws that lead to an almost everywhere stabilization of the closed-loop system to an equilibrium point while navigating a region optimally and safely (that is, the transient leading to the final equilibrium point is optimal and satisfies safety constraints). Though this problem has been studied in literature within many different communities, it still remains a challenging non-convex control problem. In our approach, under certain assumptions on the time-varying navigation density, we use Koopman and Perron-Frobenius Operator theoretic tools to transform the problem into a convex one in infinite dimensional decision variables. In particular, the cost function and the safety constraints in the transformed formulation become linear in these functional variables. Finally, we present some numerical examples to illustrate our approach, as well as discuss the current limitations and future extensions of our framework to accommodate a wider range of robotics applications.

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    Navigation in Time-Varying Densities: An Operator Theoretic Approach
  • 252.
    Della Penda, Demia
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Device-to-Device Communication in Future Cellular Networks: Resource allocation and mode selection2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The widespread use of smart devices and mobile applications is leading to a massive growth of wireless data traffic. Supporting the upcoming demands of data volume, communication rate, and system capacity requires reconsideration of the existing network architecture. Traditionally, users communicate through the base station via uplink/downlink paths. By allowing device-to-device (D2D) communication, that is, direct transmission between the users, we can enhance both efficiency and scalability of future networks. In this thesis, we address some of the challenges brought by the integration of D2D communication in cellular systems, and validate the potential of this technology by means of proper resource management solutions. Our main contributions lie in the context of mode selection, power control, and frequency/time resource allocation mechanisms. First, we investigate how the integration of D2D communication in dynamic Time Division Duplex systems can enhance the energy efficiency. We propose a joint optimization of mode selection, uplink/downlink transmission time, and power allocation to minimize the energy consumption. The optimization problem is formulated as a mixed-integer nonlinear programming problem, which is NP-hard in general. By exploiting the problem structure, we develop efficient (and for some scenarios, optimal) solutions. We complement the work with a heuristic scheme that achieves near-optimal solutions while respecting practical constraints in terms of execution times and signaling overhead. Second, we study the performance of several power control strategies applicable to D2D-enabled networks. In particular, we compare 3GPP LTE uplink power control with a distributed scheme based on utility maximization. Furthermore, to extend the application of well-known power control approaches to Rician-fading environments, we propose a power allocation scheme based on the concept of coherent-measure-of-risk. This approach allows to obtain a convex and efficiently solvable problem. Third, we study the subcarrier allocation problem in D2D-enabled networks. We maximize the total transmission rate by modeling the problem as a potential game. Nash equilibria of the game correspond to local optima of the objective function, which are found via better-response dynamic implemented with message passing approach. Finally, we propose two different applications of full-duplex technology for D2D communication. First, we present a practical mode selection algorithm that leverages only the existing control signaling to minimize the users' probability of outage. Second, we investigate how the combination of D2D relaying and full-duplex operations can improve the network coverage and the communication quality without additional infrastructure deployment.

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    Demia_PhD
  • 253.
    Della Penda, Demia
    et al.
    Ericsson AB, S-16480 Kista, Sweden..
    Abrardo, Andrea
    Univ Siena, Dept Informat Engn, I-53100 Siena, Italy.;Consorzio Nazl Interuniv Telecomunicaz, I-43124 Parma, Italy..
    Moretti, Marco
    Consorzio Nazl Interuniv Telecomunicaz, I-43124 Parma, Italy.;Univ Pisa, Dept Informat Engn, I-56122 Pisa, Italy..
    Johansson, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Distributed Channel Allocation for D2D-Enabled 5G Networks Using Potential Games2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 11195-11208Article in journal (Refereed)
    Abstract [en]

    Frequency channel allocation is a key technique for improving the performance of cellular networks. In this paper, we address the channel allocation problem for a 5G multi-cell system. We consider a heterogeneous network in which cellular users, micro-cell users, and device-to-device (D2D) communications coexist within the radio footprint of the macro cell. We maximize the aggregate transmission rate, exploiting channel diversity and managing both the inter-cell interference, typical of cellular networks and the intra-cell interference generated by the nonorthogonal transmissions of the small-cell and D2D users. By modeling the allocation problem as a potential game, whose Nash equilibria correspond to the local optima of the objective function, we propose a new decentralized solution. The convergence of our scheme is enforced by using a better response dynamic based on a message passing approach. The simulation results assess the validity of the proposed scheme in terms of convergence time and achievable rate under different settings.

  • 254.
    Della Penda, Demia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Ericsson AB, Stockholm, 164 83, Sweden.
    Wichman, Risto
    Aalto Univ, Sch Elect Engn, FI-00076 Aalto, Finland..
    Charalambous, Themistoklis
    Aalto Univ, Sch Elect Engn, FI-00076 Aalto, Finland..
    Fodor, Gabor
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Ericsson AB, Stockholm, 164 83, Sweden.
    Johansson, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    A Distributed Mode Selection Scheme for Full-Duplex Device-to-Device Communication2019In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 68, no 10, p. 10267-10271Article in journal (Refereed)
    Abstract [en]

    Networks with device-to-device(D2D) technology allow for two possible communication modes: traditional communication via the base station, and direct communication between the users. Recent studies show that in-band full-duplex(IBFD) operations can be advantageously combined with D2D communication to improve the spectral efficiency. However, no algorithms for selecting the communication mode of mobile users in IBFD networks have yet appeared in the literature. In this paper, we design a distributed mode selection scheme for users in D2D-enabled IBFD networks. The proposed scheme maximizes the users prob-ability of successful communication by leveraging only existing signaling mechanisms.

  • 255.
    Della Rossa, Matteo
    et al.
    UCLouvain, ICTEAM, Louvain La Neuve, Belgium..
    Pasquini, Mirko
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). AdBIOPRO, Competence Ctr Adv Bioprod Continuous Proc, Stockholm, Sweden..
    Angeli, David
    Imperial Coll, Dept Elect & Elect Engn, London, England.;Univ Florence, Dip Ingn Informaz, Florence, Italy..
    Continuous-time switched systems with switching frequency constraints: Path-complete stability criteria2022In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 137, p. 110099-, article id 110099Article in journal (Refereed)
    Abstract [en]

    We propose a novel Lyapunov construction for continuous-time switched systems relying on a graph theoretical Lyapunov construction. Starting with a finite family of continuously differentiable functions, suitable inequalities involving these functions and the vector fields defining the switched system are encoded in a direct and labeled graph. We then provide sufficient conditions for (asymptotic) stability subject to constrained switching times, by relying on the path-completeness of the chosen graph. The analysis is first carried out under the hypothesis of constant switching frequency. Then, the results are generalized to dwell time setting. In the case of linear dynamics, the graph formalism allows us to interpret the existing results on dwell time stability in a unified language. Some numerical examples illustrate the usefulness of the conditions.

  • 256.
    Delle Monache, M. L.
    et al.
    Univ Calif Berkeley, Dept Civil & Environm Engn, Berkeley, CA 94720 USA..
    Pasquale, C.
    Univ Genoa, Dept Informat Bioengn Robot & Syst Engn, Genoa, Italy..
    Barreau, Matthieu
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Stern, R.
    Univ Minnesota, Dept Civil Environm & Geoengn, Minneapolis, MN 55455 USA..
    New frontiers of freeway traffic control and estimation2022In: 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 6910-6925Conference paper (Refereed)
    Abstract [en]

    This article provides an overview of the classical and new techniques in traffic flow control and estimations. The overview begins with a description of the most used traffic flow models for estimation and control. Then, it shifts towards using those models for traffic flow estimation using physics-informed machine learning techniques. Lastly, it focuses on traffic flow control describing the most classical techniques and the new advancement in traffic control using autonomous vehicles.

  • 257.
    Demirel, Burak
    et al.
    Paderborn Univ, Chair Automat Control EIME, D-33098 Paderborn, Germany..
    Ghadimi, Euhanna
    Huawei Technol Sweden AB, SE-16494 Kista, Sweden..
    Quevedo, Daniel E.
    Paderborn Univ, Chair Automat Control EIME, D-33098 Paderborn, Germany..
    Johansson, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Optimal Control of Linear Systems With Limited Control Actions: Threshold-Based Event-Triggered Control2018In: IEEE Transactions on Control of Network Systems, E-ISSN 2325-5870, Vol. 5, no 3, p. 1275-1286Article in journal (Refereed)
    Abstract [en]

    We consider a finite-horizon linear-quadratic optimal control problem where only a limited number of control messages are allowed for sending from the controller to the actuator. To restrict the number of control actions computed and transmitted by the controller, we employ a threshold-based event-triggering mechanism that decides whether or not a control message needs to be calculated and delivered. Due to the nature of threshold-based event-triggering algorithms, finding the optimal control sequence requires minimizing a quadratic cost function over a nonconvex domain. In this paper, we first provide an exact solution to this nonconvex problem by solving an exponential number of quadratic programs. To reduce computational complexity, we then propose two efficient heuristic algorithms based on greedy search and the alternating direction method of multipliers technique. Later, we consider a receding horizon control strategy for linear systems controlled by event-triggered controllers, and we further provide a complete stability analysis of receding horizon control that uses finite-horizon optimization in the proposed class. Numerical examples testify to the viability of the presented design technique.

  • 258.
    Deplano, Diego
    et al.
    University of Cagliari, DIEE, Cagliari, Italy.
    Bastianello, Nicola
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Franceschelli, Mauro
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Johansson, Karl H.
    University of Cagliari, DIEE, Cagliari, Italy.
    A Unified Approach to Solve the Dynamic Consensus on the Average, Maximum, and Median Values with Linear Convergence2023In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 6442-6448Conference paper (Refereed)
    Abstract [en]

    This manuscript proposes novel distributed algorithms for solving the dynamic consensus problem in discrete-time multi-agent systems on three different objective functions: the average, the maximum, and the median. In this problem, each agent has access to an external time-varying scalar signal and aims to estimate and track a function of all the signals by exploiting only local communications with other agents. By recasting the problem as an online distributed optimization problem, the proposed algorithms are derived based on the distributed implementation of the alternating direction method of multipliers (ADMM) and are thus amenable to a unified analysis technique. A major contribution is that of proving linear convergence of these ADMM-based algorithms for the specific dynamic consensus problems of interest, for which current results could only guarantee sub-linear convergence. In particular, the tracking error is shown to converge within a bound, whereas the steady-state error is zero. Numerical simulations corroborate the theoretical findings, empirically show the robustness of the proposed algorithms to re-initialization errors, and compare their performance with that of state-of-the-art algorithms.

  • 259. Derewa, C.
    et al.
    Holt, S.
    Lally, M.
    Trybula, R.
    Yuan, C.
    Coble, Kyle
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Huang, C.
    Lunar Asset Messaging and on Orbit Navigation (LA MOON)2021In: Proceedings of the International Astronautical Congress, IAC, International Astronautical Federation, IAF , 2021Conference paper (Refereed)
    Abstract [en]

    NASA has titled its 2020 thrust for the Moon, Artemis. The increased focus on the Moon as a destination for future human and robotic expeditions necessitates general purpose navigational and communications infrastructure reducing their complexity to help establish a sustained presence. A framework through which Lunar missions can relay communications and localize their positions shifts the burden from the individual mission and enables resource allocation tailored to mission-specific goals. During the summer of 2020, student interns under the Innovation to Flight (i2F) program at the National Aeronautics and Space Administration s (NASA) Jet Propulsion Laboratory (JPL) in collaboration with the University of Colorado Boulder designed, built, and tested a prototype framework capable of providing surface assets with communication and positioning services. The team utilized the existing i2F CubeSat bus in addition to developing several CubeSat engineering development units (EDUs), a ground vehicle, and a ground station to simulate a scenario in which a lunar surface mission is supported by these services. A primary goal of the summer was to develop a method for localizing the ground vehicle through trilateration. Distances are inferred from the round-Trip time of flight (ToF) of radio signals between an asset and several elements. Signals were sent and received using LimeSDR software defined radios on-board both the ground vehicle and the EDUs; ToF and trilateration were calculated on a Qualcomm Snapdragon development board located within the LA MOON payload data system. The ModalAI chipset on the Qualcomm was instrumental in executing visual based position estimation. Communications was facilitated through a bent-pipe approach addressing the NASA requirement to provide solutions for in communication denied locations. The ground vehicle relayed information to other surface assets in addition to its ground station through the supporting constellation. This project demonstrates the feasibility of a lunar CubeSat constellation for the support of surface assets and explores packaging and operations of the components critical to trilateration and bent-pipe communication into a standard CubeSat form factor. When implemented, this framework will open a door for new surface missions designed with lower power requirements and increased operational access.

  • 260.
    Dibaji, S. M.
    et al.
    MIT, Dept Mech Engn, Cambridge, MA 02139 USA..
    Pirani, Mohammad
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Annaswamy, A. M.
    MIT, Dept Mech Engn, Cambridge, MA 02139 USA..
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Chakrabortty, A.
    North Carolina State Univ, Dept Elect Engn, Raleigh, NC USA..
    Secure Control of Wide-Area Power Systems: Confidentiality and Integrity Threats2018In: 2018 IEEE Conference on Decision and Control  (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 7269-7274, article id 8618862Conference paper (Refereed)
    Abstract [en]

    A cyber-physical model for wide-area control of power systems is considered, where the state variables of each generator are measured and sent to the cyber-network and the corresponding control inputs are computed distributively. The secure control of such wide-area power systems is considered in the presence of cyber attacks that introduce threats that compromise their integrity and confidentiality. Detection, prevention, and resilience for these attacks and algorithms for accomplishing these goals are proposed. In particular, an algorithm to overcome confidentiality attacks of the underlying control gains is presented. Also proposed is an algorithm for defense against integrity attacks that might take place on the cyber-network. For this purpose, a resilient information retrieval approach is leveraged which recovers the true state variables despite the malicious attacks on both virtual machines and communication links. The retrieved states are then used to detect possible attacks on phasor measurement units (PMU) in the next time-step. Simulation studies are included to validate our proposed approaches.

  • 261.
    Dibaji, Seyed Mehran
    et al.
    MIT, Dept Mech Engn, Cambridge, MA 02139 USA..
    Pirani, Mohammad
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Flamholz, David Bezalel
    MIT, Dept Mech Engn, Cambridge, MA 02139 USA..
    Annaswamy, Anuradha M.
    MIT, Dept Mech Engn, Cambridge, MA 02139 USA..
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Chakrabortty, Aranya
    North Carolina State Univ, Dept Elect Engn, Raleigh, NC USA..
    A systems and control perspective of CPS security2019In: Annual Reviews in Control, ISSN 1367-5788, E-ISSN 1872-9088, Vol. 47, p. 394-411Article, review/survey (Refereed)
    Abstract [en]

    The comprehensive integration of instrumentation, communication, and control into physical systems has led to the study of Cyber-Physical Systems (CPSs), a field that has recently garnered increased attention. A key concern that is ubiquitous in CPS is a need to ensure security in the face of cyber attacks. In this paper, we carry out a survey of systems and control methods that have been proposed for the security of CPS. We classify these methods into three categories based on the type of defense proposed against the cyberattacks: prevention, resilience, and detection & isolation. A unified threat assessment metric is proposed in order to evaluate how CPS security is achieved in each of these three cases. Also surveyed are the risk assessment tools and the effect of network topology on CPS security. Furthermore, an emphasis has been placed on power and transportation applications in the overall survey.

  • 262.
    Djehiche, Boualem
    et al.
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Mazhar, Othmane
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Finite impulse response models: A non-asymptotic analysis of the least squares estimator2021In: Bernoulli, ISSN 1350-7265, E-ISSN 1573-9759, Vol. 27, no 2, p. 976-1000Article in journal (Refereed)
    Abstract [en]

    We consider a finite impulse response system with centered independent sub-Gaussian design covariates and noise components that are not necessarily identically distributed. We derive non-asymptotic near-optimal estimation and prediction bounds for the least squares estimator of the parameters. Our results are based on two concentration inequalities on the norm of sums of dependent covariate vectors and on the singular values of their covariance operator that are of independent value on their own and where the dependence arises from the time shift structure of the time series. These results generalize the known bounds for the independent case.

  • 263.
    do Nascimento, Allan Andre
    et al.
    KTH.
    Feyzmahdavian, Hamid Reza
    ABB Corp Res, Vasteras, Sweden..
    Johansson, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Garcia-Gabin, Winston
    ABB Corp Res, Vasteras, Sweden..
    Tervo, Kalevi
    ABB Marine & Ports, Helsinki, Finland..
    Tube-based Model Predictive Control for Dynamic Positioning of Marine Vessels2019In: IFAC PAPERSONLINE, ELSEVIER , 2019, Vol. 52, no 21, p. 33-38Conference paper (Refereed)
    Abstract [en]

    This paper focuses on the design of a robust model predictive control law for dynamic positioning (DP) of marine vessels in the presence of actuator saturation and environmental disturbances. The proposed solution is a tube-based MPC ensuring robustness and constraint fulfillment. Formulation of the tube-based MPC relies on a sufficient robust invariant set condition, along with a linear matrix inequality (LMI) synthesis procedure, and an efficient analytical Pontryagin set difference computation. Simulation results show the effectiveness and satisfactory behaviour of the proposed controller. 

  • 264.
    Doostmohammadian, Mohammadreza
    et al.
    Aalto University, School of Electrical Engineering, Finland.
    Aghasi, Alireza
    Georgia State University, GA, USA.
    Rikos, Apostolos
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Grammenos, Andreas
    Alan Turing Institute, London, UK; University of Cyprus, Department of Electrical and Computer Engineering, Cyprus.
    Kalyvianaki, Evangelia
    University of Cambridge, Department of Computer Science and Technology, Cambridge, UK.
    Hadjicostis, Christoforos N.
    University of Cyprus, Department of Electrical and Computer Engineering, Cyprus.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Charalambous, Themistoklis
    Aalto University, School of Electrical Engineering, Finland.
    Distributed CPU Scheduling Subject to Nonlinear Constraints2022In: 2022 IEEE Conference on Control Technology and Applications, CCTA 2022, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 746-751Conference paper (Refereed)
    Abstract [en]

    This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints. In many applications, it is required (or desirable) that the solution be anytime feasible in terms of satisfying the sum-preserving global constraint. Motivated by this, sufficient conditions on the nonlinear mapping for anytime feasibility (or non-asymptotic feasibility) are addressed in this paper. For the two proposed distributed solutions, one converges over directed weight-balanced networks and the other one over undirected networks. In particular, we elaborate on uniform quantization and discuss the notion of ϵ-accurate solution, which gives an estimate of how close we can get to the exact optimizer subject to different quantization levels. This work, further, handles general (possibly non-quadratic) strictly convex objective functions with application to CPU allocation among a cloud of data centers via distributed solutions. The results can be used as a coordination mechanism to optimally balance the tasks and CPU resources among a group of networked servers while addressing quantization or limited server capacity.

  • 265.
    Dos Santos Miraldo, Pedro
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). LARSyS, Institute for Systems and Robotics (ISR/IST), Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal.
    Cardoso, Joao R.
    Coimbra Polytechnic Institute (ISEC) and Center for Mathematics, University of Coimbra, Coimbra, Portugal.
    On the Generalized Essential Matrix Correction: An Efficient Solution to the Problem and Its Applications2020In: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, Vol. 62, no 8, p. 1107-1120Article in journal (Refereed)
    Abstract [en]

    This paper addresses the problem of finding the closest generalized essential matrix from a given 6 × 6 matrix, with respect to the Frobenius norm. To the best of our knowledge, this nonlinear constrained optimization problem has not been addressed in the literature yet. Although it can be solved directly, it involves a large number of constraints, and any optimization method to solve it would require much computational effort. We start by deriving a couple of unconstrained formulations of the problem. After that, we convert the original problem into a new one, involving only orthogonal constraints, and propose an efficient algorithm of steepest descent type to find its solution. To test the algorithms, we evaluate the methods with synthetic data and conclude that the proposed steepest descent-type approach is much faster than the direct application of general optimization techniques to the original formulation with 33 constraints and to the unconstrained ones. To further motivate the relevance of our method, we apply it in two pose problems (relative and absolute) using synthetic and real data.

  • 266.
    Dos Santos Miraldo, Pedro
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Saha, S.
    Ramalingam, S.
    Minimal solvers for mini-loop closures in 3D multi-scan alignment2019In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers (IEEE) , 2019, p. 9691-9700Conference paper (Refereed)
    Abstract [en]

    3D scan registration is a classical, yet a highly useful problem in the context of 3D sensors such as Kinect and Velodyne. While there are several existing methods, the techniques are usually incremental where adjacent scans are registered first to obtain the initial poses, followed by motion averaging and bundle-adjustment refinement. In this paper, we take a different approach and develop minimal solvers for jointly computing the initial poses of cameras in small loops such as 3-, 4-, and 5-cycles. Note that the classical registration of 2 scans can be done using a minimum of 3 point matches to compute 6 degrees of relative motion. On the other hand, to jointly compute the 3D registrations in n-cycles, we take 2 point matches between the first n-1 consecutive pairs (i.e., Scan 1 & Scan 2,... , and Scan n-1 & Scan n) and 1 or 2 point matches between Scan 1 and Scan n. Overall, we use 5, 7, and 10 point matches for 3-, 4-, and 5-cycles, and recover 12, 18, and 24 degrees of transformation variables, respectively. Using simulations and real-data we show that the 3D registration using mini n-cycles are computationally efficient, and can provide alternate and better initial poses compared to standard pairwise methods.

  • 267.
    Drollinger, Nadine
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Developing a System for Robust Planning using Linear Temporal Logic2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Human robot-collaborative search missions have gotten more and more attention in recent years.Especially in scenarios where the robot first scouts the scene before sending in human agents. Thissaves time and avoids unnecessary risks for the human agents. One possible configuration of such arescue team is, a human operator instructing a unmanned aerial vehicle (UAV) via speech-commandshow to traverse through an environment to investigate areas of interest. A first step to address thisproblem is presented in this master thesis by developing a framework for mapping temporal logicinstructions to physical motion of a UAV.The fact that natural language has a strong resemblance to the logic formalism of Linear-TemporalLogic (LTL) is exploited. Constraints expressed as an LTL-formula are imposed on a provided labeledmap of the environment. An LTL-to-cost-map converter including a standard input-skeleton is developed.Respective cost maps are obtained and a satisfaction-measure of fulfilling these constraints ispresented. The input-skeleton and the map-converter are then combined with a cost-map-based pathplanning algorithm in order to obtain solution sets. A clarification request is created such that theoperator can choose which solution set should be executed. The proposed framework is successivelyvalidated, first by MATLAB-experiments to ensure the validity of the cost-map-creation followed bysimulation experiments in ROS incorporating the entire framework. Finally, a real-world experimentis performed at the SML to validate the proposed framework.

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  • 268.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Gkatzikis, Lazaros
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    On Maximizing Sensor Network Lifetime by Energy Balancing2018In: IEEE Transactions on Control of Network Systems, E-ISSN 2325-5870, Vol. 5, no 3Article in journal (Refereed)
    Abstract [en]

    Many physical systems, such as water/electricity distribution networks, are monitored by battery-powered wireless-sensor networks (WSNs). Since battery replacement of sensor nodes is generally difficult, long-term monitoring can be only achieved if the operation of the WSN nodes contributes to long WSN lifetime. Two prominent techniques to long WSN lifetime are 1) optimal sensor activation and 2) efficient data gathering and forwarding based on compressive sensing. These techniques are feasible only if the activated sensor nodes establish a connected communication network (connectivity constraint), and satisfy a compressive sensing decoding constraint (cardinality constraint). These two constraints make the problem of maximizing network lifetime via sensor node activation and compressive sensing NP-hard. To overcome this difficulty, an alternative approach that iteratively solves energy balancing problems is proposed. However, understanding whether maximizing network lifetime and energy balancing problems are aligned objectives is a fundamental open issue. The analysis reveals that the two optimization problems give different solutions, but the difference between the lifetime achieved by the energy balancing approach and the maximum lifetime is small when the initial energy at sensor nodes is significantly larger than the energy consumed for a single transmission. The lifetime achieved by energy balancing is asymptotically optimal, and that the achievable network lifetime is at least 50% of the optimum. Analysis and numerical simulations quantify the efficiency of the proposed energy balancing approach.

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  • 269.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering. KTH Royal Inst Technol, Div Network & Syst Engn, Stockholm, Sweden..
    Magnusson, Sindri
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    The Internet of Things as a Deep Neural Network2020In: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 58, no 9, p. 20-25Article in journal (Refereed)
    Abstract [en]

    An important task in the Internet of Things (IoT) is field monitoring, where multiple IoT nodes take measurements and communicate them to the base station or the cloud for processing, inference, and analysis. When the measurements are high-dimensional (e.g., videos or time-series data), IoT networks with limited bandwidth and low-power devices may not be able to support such frequent transmissions with high data rates. To ensure communication efficiency, this article proposes to model the measurement compression at IoT nodes and the inference at the base station or cloud as a deep neural network (DNN). We propose a new framework where the data to be transmitted from nodes are the intermediate outputs of a layer of the DNN. We show how to learn the model parameters of the DNN and study the trade-off between the communication rate and the inference accuracy. The experimental results show that we can save approximately 96 percent transmissions with only a degradation of 2.5 percent in inference accuracy, which shows the potentiality to enable many new IoT data analysis applications that generate a large amount of measurements.

  • 270.
    Egidio, Lucas N.
    et al.
    Catholic Univ Louvain, ICTEAM, INMA, B-1348 Louvain La Neuve, Belgium..
    Hansson, Anders
    Linköping Univ, Dept Elect Engn, S-58183 Linköping, Sweden..
    Wahlberg, Bo
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Learning the Step-size Policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm2021In: 2021 international joint conference on neural networks (IJCNN), Institute of Electrical and Electronics Engineers (IEEE) , 2021Conference paper (Refereed)
    Abstract [en]

    We consider the problem to learn a step-size policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. This is a limited computational memory quasi-Newton method widely used for deterministic unconstrained optimization. However, L-BFGS is currently avoided in large-scale problems for requiring step sizes to be provided at each iteration. Current methodologies for the step size selection for L-BFGS use heuristic tuning of design parameters and massive re-evaluations of the objective function and gradient to find appropriate step-lengths. We propose a neural network architecture with local information of the current iterate as the input. The step-length policy is learned from data of similar optimization problems, avoids additional evaluations of the objective function, and guarantees that the output step remains inside a pre-defined interval. The corresponding training procedure is formulated as a stochastic optimization problem using the backpropagation through time algorithm. The performance of the proposed method is evaluated on the training of image classifiers for the MNIST database for handwritten digits and for CIFAR-10. The results show that the proposed algorithm outperforms heuristically tuned optimizers such as ADAM, RMSprop, L-BFGS with a backtracking line search, and L-BFGS with a constant step size. The numerical results also show that a learned policy can be used as a warm-start to train new policies for different problems after a few additional training steps, highlighting its potential use in multiple large-scale optimization problems.

  • 271.
    Elfeky, Ahmed
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Methods of calibration for different functions of a SCR-system2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The goal of this research is to try and compare different methods ofcalibration in order to tune the parameters of the pumping and tankheater monitoring functions of the AdBlue Delivery Module of a SelectiveCatalytic Reduction (SCR) system. The goal of the SCR systemis to reduce the emission of NOx gases, which are considered as greenhousegases.In a first step, while calibrating the parameters of the pumping function,a real-time calibration method has been used. The advantage inthis process is that a detailed model of the system is not needed totune it. Then, the tank heater monitoring function has been calibratedthrough simulations. The understanding of the system is better in thiscase, which could help tuning it more effectively.The results shows that both methods should ensure the proper functioningof the system. However, the parameters found in this studycould not be totally approved without being tested on vehicle, in reallifeconditions. Moreover, as the priority is to avoid the malfunctionof the system, the chosen parameters might not be the optimal ones interms of performance.With these two methods, most of the systems could be calibrated. Thechoice of the method should be done according to the initial level ofknowledge of the object of study

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  • 272.
    El-Hawwary, Mohamed, I
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Department of Electrical Power Engineering,Faculty of Engineering, Cairo University, Giza,Egypt.
    Hierarchic distributed stabilization of a class of three-dimensional formations for underactuated agents2021In: IET Control Theory & Applications, ISSN 1751-8644, E-ISSN 1751-8652, Vol. 15, no 3, p. 472-488Article in journal (Refereed)
    Abstract [en]

    The paper presents a distributed hierarchic control design solving a formations problem in three dimension. The agents are modelled as thrust-underactuated rigid bodies. The class of formations addressed encompasses path following of closed convex paths with shape, size, orientation, relative displacements and relative agents' on-path angular positions all seen as formation parameters. This problem can be seen as a generalization of circular formation problems which acquired particular attention in the field of multi-agents, and the agents model used is one that usually models certain unmanned aerial vehicles and other autonomous vehicles. The solution relies on reduction-based set stabilization where the problem is broken down into simpler nested sub-problems. The results illustrate how addressing complex control problems using hierarchical set stabilization is natural, simplifies the solution, and provides useful properties. The results are illustrated via simulations.

  • 273.
    El-Hawwary, Mohamed, I
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Hierarchic Stabilization of Flying Convex-Path-Following Formations Part I: Fully-Actuated Agents2019In: IFAC PAPERSONLINE, ELSEVIER , 2019, Vol. 52, no 12, p. 346-351Conference paper (Refereed)
    Abstract [en]

    The paper presents a hierarchical approach for distributed stabilization of a class of flying formations. The agents are considered as rigid bodies in three-dimensional space. The formation is composed of three aspects: a path following aspect where the agents are required to follow similar smooth Jordan paths that are strictly convex; and two geometric aspects that specify the desired relative displacements of the paths, and the relative positions of the agents on these paths. The solution is based on tools of set stabilization and reduction. The modularity of the approach allows for achieving multiple specifications simultaneously, and for extending, in Part II of this paper, the solution to handle underactuation. The solution is illustrated through simulations, and the relevance of the results in aerial and space vehicles applications is discussed. Copyright

  • 274.
    El-Hawwary, Mohamed, I
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Hierarchic Stabilization of Flying Convex-Path-Following Formations Part II: Underactuated Agents2019In: IFAC PAPERSONLINE, ELSEVIER , 2019, Vol. 52, no 12, p. 352-357Conference paper (Refereed)
    Abstract [en]

    In this paper the flying convex-path-following formations problem (FCxPFF) is solved for two cases of underactuated rigid bodies. In the first case the the rigid bodies have a single degree of underactuation with two thrusts and three torques. In the second, they have two degrees of underactuation with a single thrust. The solution builds on the one developed for fully-actuated agents in Part I of the paper. In addition, the way the solution is tailored for underactuation relies on further utilization of hierarchic set stabilization, and reduction. Additional remarks on the benefits of the approach, and simulation results of the proposed solutions are presented. Copyright

  • 275.
    El-Hawwary, Mohamed, I
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Hierarchy for circular orbit stabilization of underactuated satellites with application to distributed formations2020In: CCTA 2020 - 4th IEEE Conference on Control Technology and Applications, Institute of Electrical and Electronics Engineers Inc. , 2020, p. 174-179Conference paper (Refereed)
    Abstract [en]

    The paper addresses circular orbits stabilization for a class of thrust-underactuated satellites, utilizing smooth feedbacks. The problem is first solved in a path-following sense which is pertinent to stabilization of distributed formations. Two orthogonal thrusts are used where, broadly, one is used to stabilize the satellite to the desired orbital plane, and the second is used to stabilize the desired orbit on that plane. Control design follows a five-step hierarchy of stabilizing five nested sets. A main advantage of the approach is its amenability to modification. By redesigning only the last step of the hierarchy, the result is extended to stabilizing time parametrized orbits, i.e. trajectory tracking, and distributed formations of not necessarily co-planar orbits with or without temporal requirements. The results are illustrated through simulations.

  • 276.
    El-Hawwary, Mohamed, I
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Mårtensson, Jonas
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Distributing Potential Games on Graphs Part I. Game formulation2020In: IFAC PAPERSONLINE, Elsevier BV , 2020, Vol. 53, no 2, p. 6697-6702Conference paper (Refereed)
    Abstract [en]

    The paper presents the problem of distributing potential games over communication graphs. Suppose a potential game can be designed for a group of agents (players) where each has access to all others' actions (strategies). The paper shows how to design a corresponding potential game for these agents if the full information assumption is replaced with communication over a network depicted by undirected graphs with certain properties. A state-based formulation for potential games is utilized. This provides degrees of freedom to handle the previous information limitation. Notions of Nash's equilibria for the developed game (called here distributed potential game) are presented, and relations between these equilibria and those of the full information game are studied. In part II of the paper learning Nash equilibria for the newly developed game is studied. The development focuses on providing a way to utilize available algorithms of the full information game. The motivation for the results comes from a platoon matching problem for heavy duty vehicles. Utilizing the newly developed distributed game, recent results based on potential games can be extended, providing a basis for an on-the-go strategy where platoon matching on road networks can be solved locally.

  • 277.
    El-Hawwary, Mohamed, I
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Mårtensson, Jonas
    KTH, School of Engineering Sciences (SCI), Applied Physics, Biomedical and X-ray Physics. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Distributing Potential Games on Graphs Part II. Learning with application to platoon matching2020In: IFAC PAPERSONLINE, Elsevier BV , 2020, Vol. 53, no 2, p. 6703-6708Conference paper (Refereed)
    Abstract [en]

    In part I of the paper the problem of distributing potential games over undirected graphs was formulated. A restricted information potential game was designed using state-based formulation. Here, learning Nash equilibria for this game is studied. An algorithm is developed with mainly two phases, an estimation phase and a learning phase. The setting allows for available learning methods of the full information game to be directly incorporated in the learning phase. The result matches the outcome (i.e. converges to the same equilibria) of the full information game. In addition, the design takes into account considerations of convergence time, and synchrony of actions update. The developed distributed game and learning algorithm are used to solve a platoon matching problem for heavy duty vehicles. This serves two objectives. First, it provides a motivation for the presented gaming results. Second, the problem addressed can facilitate platoon matching where it provides a basis for an on-the-go strategy. 

  • 278.
    Elton, Augustus
    et al.
    School of Engineering, University of Newcastle, Callaghan, Australia.
    González, Rodrigo A.
    Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
    Welsh, James S.
    School of Engineering, University of Newcastle, Callaghan, Australia.
    Oomen, Tom
    Department of Mechanical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Delft Center for Systems and Control, Delft University of Technology, The Netherlands.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Blind Nonparametric Estimation of SISO Continuous-time Systems2023In: IFAC-PapersOnLine, Elsevier BV , 2023, Vol. 56, p. 4222-4227Conference paper (Refereed)
    Abstract [en]

    Blind system identification is aimed at finding parameters of a system model when the input is inaccessible. In this paper, we propose a blind system identification method that delivers a single-input single-output, continuous-time model in a nonparametric kernel form. We take advantage of the representer theorem to form a joint maximum a posteriori estimator of the input and system impulse response. The identified system model and input are optimised in sequence to overcome the blind problem with generalised cross validation used to select appropriate hyperparameters given some fixed input sequence. We demonstrate via Monte Carlo simulations the accuracy of the method in terms of estimating the input.

  • 279.
    Elton, Augustus
    et al.
    College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW, Australia.
    González, Rodrigo A.
    Eindhoven University of Technology, Department of Mechanical Engineering, Eindhoven, The Netherlands.
    Welsh, James S.
    College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW, Australia.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Fu, Minyue
    College of Engineering, Science and Environment, University of Newcastle, Callaghan, NSW, Australia.
    Parametric Continuous-Time Blind System Identification2023In: 2023 62nd IEEE Conference on Decision and Control, CDC2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 1474-1479Conference paper (Refereed)
    Abstract [en]

    In this paper, the blind system identification problem for continuous-time systems is considered. A direct continuous-time estimator is proposed by utilising a state-variable-filter least squares approach. In the proposed method, coupled terms between the numerator polynomial of the system and input parameters appear in the parameter vector which are subsequently separated using a rank-1 approximation. An algorithm is then provided for the direct identification of a single-input single-output linear time-invariant continuous-time system which is shown to satisfy the property of correctness under some mild conditions. Monte Carlo simulations demonstrate the performance of the algorithm and verify that a model and input signal can be estimated to a proportion of their true values.

  • 280.
    Emad, Sawsan
    et al.
    Ain Shams Univ, Comp & Syst Dept, Cairo, Egypt..
    Alanwar, Amr
    Jacobs Univ, Bremen, Germany..
    Alkabani, Yousra
    Halmstad Univ, Halmstad, Sweden..
    El-Kharashi, M. Watheq
    Ain Shams Univ, Comp & Syst Dept, Cairo, Egypt..
    Sandberg, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Privacy Guarantees for Cloud-based State Estimation using Partially Homomorphic Encryption2022In: 2022 European Control Conference (ECC), IEEE , 2022, p. 98-105Conference paper (Refereed)
    Abstract [en]

    The privacy aspect of state estimation algorithms has been drawing high research attention due to the necessity for a trustworthy private environment in cyber-physical systems. These systems usually engage cloud-computing platforms to aggregate essential information from spatially distributed nodes and produce desired estimates. The exchange of sensitive data among semi-honest parties raises privacy concerns, especially when there are coalitions between parties. We propose two privacy-preserving protocols using Kalman filter and partially homomorphic encryption of the measurements and estimates while exposing the covariances and other model parameters. We prove that the proposed protocols achieve satisfying computational privacy guarantees against various coalitions based on formal cryptographic definitions of indistinguishability. We evaluate the proposed protocols to demonstrate their efficiency using data from a real testbed.

  • 281.
    Emanuelsson, William
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Riveiros, Alejandro Penacho
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Li, Yuchao
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Mårtensson, Jonas
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Multiagent Rollout with Reshuffling for Warehouse Robots Path Planning2023In: IFAC-PapersOnLine, Elsevier B.V. , 2023, Vol. 56, p. 3027-3032Conference paper (Refereed)
    Abstract [en]

    Efficiently solving path planning problems for a large number of robots is critical to the successful operation of modern warehouses. The existing approaches adopt classical shortest path algorithms to plan in environments whose cells are associated with both space and time in order to avoid collision between robots. In this work, we achieve the same goal by means of simulation in a smaller static environment. Built upon the new framework introduced in (Bertsekas, 2021a), we propose multiagent rollout with reshuffling algorithm, and apply it to address the warehouse robots path planning problem. The proposed scheme has a solid theoretical guarantee and exhibits consistent performance in our numerical studies. Moreover, it inherits from the generic rollout methods the ability to adapt to a changing environment by online replanning, which we demonstrate through examples where some robots malfunction.

  • 282.
    Eriksson, Lars
    et al.
    Linkoping Univ, Dept Elect Engn, Vehicular Syst, SE-58183 Linkoping, Sweden..
    Thomasson, Andreas
    Linkoping Univ, Dept Elect Engn, Vehicular Syst, SE-58183 Linkoping, Sweden..
    Ekberg, Kristoffer
    Linkoping Univ, Dept Elect Engn, Vehicular Syst, SE-58183 Linkoping, Sweden..
    Reig, Alberto
    Univ Politecn Valencia, E-46022 Valencia, Spain..
    Eifert, Mark
    Ford Res & Innovat Ctr, Susterfeldstr 200, D-52072 Aachen, Germany..
    Donatantonio, Fabrizio
    Univ Salerno, Dept Ind Engn, Energy & Prop Syst Lab, Via Giovanni Paolo II 132, I-84084 Fisciano, SA, Italy..
    D'Amato, Antonio
    Univ Salerno, Dept Ind Engn, Energy & Prop Syst Lab, Via Giovanni Paolo II 132, I-84084 Fisciano, SA, Italy..
    Arsie, Ivan
    Univ Salerno, Dept Ind Engn, Energy & Prop Syst Lab, Via Giovanni Paolo II 132, I-84084 Fisciano, SA, Italy..
    Pianese, Cesare
    Univ Salerno, Dept Ind Engn, Energy & Prop Syst Lab, Via Giovanni Paolo II 132, I-84084 Fisciano, SA, Italy..
    Otta, Pavel
    Czech Tech Univ, Dept Control Engn, Prague, Czech Republic..
    Henriksson, Manne
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Voegele, Ulrich
    TH Ingolstadt, Ingolstadt, Germany..
    Endisch, Christian
    TH Ingolstadt, Ingolstadt, Germany..
    Look-ahead controls of heavy duty trucks on open roads - six benchmark solutions2019In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 83, p. 45-66Article in journal (Refereed)
    Abstract [en]

    A benchmark problem for fuel efficient control of a truck on a given road profile has been formulated and solved. Six different solution strategies utilizing varying degrees of off-line and on-line computations are described and compared. A vehicle model is used to benchmark the solutions on different driving missions. The vehicle model was presented at the IFAC AAC2016 symposium and is compiled from model components validated in previous research projects. The driving scenario is provided as a road slope profile and a desired trip time. The problem to solve is a combination of engine-, driveline- and vehicle-control while fulfilling demands on emissions, driving time, legislative speed, and engine protections. The strength of this publication is the collection of all six different solutions in one paper. This paper is intended to provide a starting point for practicing engineers or researchers who work with optimal and/or model based vehicle control.

  • 283.
    Everitt, Niklas
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Galrinho, Miguel
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Open-loop asymptotically efficient model reduction with the Steiglitz–McBride method2018In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 89, p. 221-234Article in journal (Refereed)
    Abstract [en]

    In system identification, it is often difficult to use a physical intuition when choosing a noise model structure. The importance of this choice is that, for the prediction error method (PEM) to provide asymptotically efficient estimates, the model orders must be chosen according to the true system. However, if only the plant estimates are of interest and the experiment is performed in open loop, the noise model can be over-parameterized without affecting the asymptotic properties of the plant. The limitation is that, as PEM suffers in general from non-convexity, estimating an unnecessarily large number of parameters will increase the risk of getting trapped in local minima. Here, we consider the following alternative approach. First, estimate a high-order ARX model with least squares, providing non-parametric estimates of the plant and noise model. Second, reduce the high-order model to obtain a parametric model of the plant only. We review existing methods to do this, pointing out limitations and connections between them. Then, we propose a method that connects favorable properties from the previously reviewed approaches. We show that the proposed method provides asymptotically efficient estimates of the plant with open-loop data. Finally, we perform a simulation study suggesting that the proposed method is competitive with state-of-the-art methods.

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  • 284. Fallgren, Mikael
    et al.
    Abbas, Taimoor
    Allio, Sylvain
    Alonso-Zarate, Jesus
    Fodor, Gabor
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Gallo, Laurent
    Kousaridas, Apostolos
    Li, Yilin
    Li, Zexian
    Li, Zhongfeng
    Luo, Jian
    Mahmoodi, Toktam
    Svensson, Tommy
    Vivier, Guillaume
    Multicast and Broadcast Enablers for High-Performing Cellular V2X Systems2019In: IEEE transactions on broadcasting, ISSN 0018-9316, E-ISSN 1557-9611, Vol. 65, no 2, p. 454-463Article in journal (Refereed)
    Abstract [en]

    This paper focuses on capabilities enabled by 5G connectivity in the cooperative, connected and autonomous cars, and elaborates on two technical enablers. One of the technical enablers consists of a beamformed broadcast/multicast technology that builds on adaptive and robust beam management techniques at the air interface. The other proposed technical component aims to improve the end-to-end architectural design of 5G networks to enable efficient broadcast and multicast transmissions for vehicle-to-anything services. Finally, the key results of multicast and broadcast technical components are described and ongoing and future areas of work and research are detailed.

  • 285.
    Fan, Zhenan
    et al.
    Univ British Columbia, Vancouver, BC, Canada..
    Fang, Huang
    Univ British Columbia, Vancouver, BC, Canada..
    Zhou, Zirui
    Huawei Technol Canada Co, Markham, ON, Canada..
    Pei, Jian
    Simon Fraser Univ, Burnaby, BC, Canada..
    Friedlander, Michael P.
    Univ British Columbia, Vancouver, BC, Canada..
    Liu, Changxin
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Zhang, Yong
    Huawei Technol Canada Co, Markham, ON, Canada..
    Improving Fairness for Data Valuation in Horizontal Federated Learning2022In: 38th IEEE International Conference on Data Engineering, ICDE 2022, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 2440-2453Conference paper (Refereed)
    Abstract [en]

    Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federated learning depends largely on the participation of data owners. To sustain and encourage data owners' participation, it is crucial to fairly evaluate the quality of the data provided by the data owners as well as their contribution to the final model and reward them correspondingly. Federated Shapley value, recently proposed by Wang et al. [Federated Learning, 2020], is a measure for data value under the framework of federated learning that satisfies many desired properties for data valuation. However, there are still factors of potential unfairness in the design of federated Shapley value because two data owners with the same local data may not receive the same evaluation. We propose a new measure called completed federated Shapley value to improve the fairness of federated Shapley value. The design depends on completing a matrix consisting of all the possible contributions by different subsets of the data owners. It is shown under mild conditions that this matrix is approximately low-rank by leveraging concepts and tools from optimization. Both theoretical analysis and empirical evaluation verify that the proposed measure does improve fairness in many circumstances.

  • 286. Fang, Mengyuan
    et al.
    Galrinho, Miguel
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Recursive Identification Based on Weighted Null-Space Fitting2017In: 2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2017Conference paper (Refereed)
    Abstract [en]

    Algorithms for online system identification consist in updating the estimated model while data are being collected. A standard method for online identification of structured models is the recursive prediction error method (PEM). The problem is that PEM does not have an exact recursive formulation, and the need to rely on approximations makes recursive PEM prone to convergence problems. In this paper, we propose a recursive implementation of weighted null-space fitting, an asymptotically efficient method for identification of structured models. Consisting only of (weighted) least-squares steps, the recursive version of the algorithm has the same convergence and statistical properties of the off-line version. We illustrate these properties with a simulation study, where the proposed algorithm always attains the performance of the off-line version, while recursive PEM often fails to converge.

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  • 287.
    Fang, Mengyuan
    et al.
    Zhejiang Sci Tech Univ, Zhejiang Engn Lab Ind Internet Commun Technol, Hangzhou, Peoples R China.;Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang, Peoples R China..
    Galrinho, Miguel
    OHB Sweden AB, Kista, Sweden..
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Recursive Weighted Null-Space Fitting Method for Identification of Multivariate Systems2021In: IFAC PAPERSONLINE, ELSEVIER , 2021, Vol. 54, no 7, p. 345-350Conference paper (Refereed)
    Abstract [en]

    Recursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.

  • 288.
    Fang, Song
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Ishii, Hideaki
    Tokyo Inst Technol, Dept Comp Sci, Tokyo, Japan..
    Chen, Jie
    City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China..
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    A Frequency-Domain Characterization of Optimal Error Covariance for the Kalman-Bucy Filter2018In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2018, p. 6366-6371Conference paper (Refereed)
    Abstract [en]

    In this paper, we discover that the trace of the division of the optimal output estimation error covariance over the noise covariance attained by the Kalman-Bucy filter can be explicitly expressed in terms of the plant dynamics and noise statistics in a frequency-domain integral characterization. Towards this end, we examine the algebraic Riccati equation associated with Kalman-Bucy filtering using analytic function theory and relate it to the Bode integral. Our approach features an alternative, frequency-domain framework for analyzing algebraic Riccati equations and reduces to various existing related results.

  • 289.
    Fang, Song
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Sandberg, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Ishii, Hideaki
    Tokyo Inst Technol, Dept Comp Sci, Yokohama, Kanagawa, Japan..
    Two-Way Coding in Control Systems Under Injection Attacks: From Attack Detection to Attack Correction2019In: ICCPS '19: PROCEEDINGS OF THE 2019 10TH ACM/IEEE INTERNATIONAL CONFERENCE ON CYBER-PHYSICAL SYSTEMS / [ed] Ramachandran, GS Ortiz, J, Association for Computing Machinery (ACM) , 2019, p. 141-150Conference paper (Refereed)
    Abstract [en]

    In this paper, we introduce the method of two-way coding, a concept originating in communication theory characterizing coding schemes for two-way channels, into (networked) feedback control systems under injection attacks. We first show that the presence of two-way coding can distort the perspective of the attacker on the control system. In general, the distorted viewpoint on the attacker side as a consequence of two-way coding will facilitate detecting the attacks, or restricting what the attacker can do, or even correcting the attack effect. In the particular case of zero-dynamics attacks, if the attacks are to be designed according to the original plant, then they will be easily detected; while if the attacks are designed with respect to the equivalent plant as viewed by the attacker, then under the additional assumption that the plant is stabilizable by static output feedback, the attack effect may be corrected in steady state.

  • 290.
    Fang, Song
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Skoglund, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Ishii, H.
    Zhu, Q.
    Generic Variance Bounds on Estimation and Prediction Errors in Time Series Analysis: An Entropy Perspective2019In: 2019 IEEE Information Theory Workshop, ITW 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, article id 8989240Conference paper (Refereed)
    Abstract [en]

    In this paper, we obtain generic bounds on the variances of estimation and prediction errors in time series analysis via an information-theoretic approach. It is seen in general that the error bounds are determined by the conditional entropy of the data point to be estimated or predicted given the side information or past observations. Additionally, we discover that in order to achieve the prediction error bounds asymptotically, the necessary and sufficient condition is that the 'innovation' is asymptotically white Gaussian. When restricted to Gaussian processes and 1-step prediction, our bounds are shown to reduce to the Kolmogorov-Szegö formula and Wiener-Masani formula known from linear prediction theory.

  • 291.
    Farjadnia, Mahsa
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    Alanwar, Amr
    Jacobs University Bremen, Bremen, Germany.
    Niazi, Muhammad Umar B.
    Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, USA.
    Molinari, Marco
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Robust Data-Driven Predictive Control of Unknown Nonlinear Systems Using Reachability Analysis2023Conference paper (Refereed)
    Abstract [en]

    This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using an explicit nonlinear system model. Although the process and measurement noise are bounded, the statistical properties of the noise are not required to be known. By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate reachable sets of an unknown nonlinear system. Then, we propose a data-driven predictive control approach to compute safe and robust control policies from noisy online data. The constraints are guaranteed in the control phase with robust safety margins through the effective use of the predicted output reachable set obtained in the learning phase. Finally, a numerical example validates the efficacy of the proposed approach and demonstrates comparable performance with a model-based predictive control approach.

  • 292.
    Farjadnia, Mahsa
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    Alanwar, Amr
    Niazi, Muhammad Umar B.
    Molinari, Marco
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Robust data-driven predictive control of unknown nonlinear systems using reachability analysis2023In: European Journal of Control, ISSN 09473580Article in journal (Refereed)
    Abstract [en]

    This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using an explicit nonlinear system model. Although the process and measurement noise are bounded, the statistical properties of the noise are not required to be known. By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate exact reachable sets of an unknown nonlinear system. Then, we propose a data-driven predictive control approach to compute safe and robust control policies from noisy online data. The constraints are guaranteed in the control phase with robust safety margins by effectively using the predicted output reachable set obtained in the learning phase. Finally, a numerical example validates the efficacy of the proposed approach and demonstrates comparable performance with a model-based predictive control approach.

  • 293.
    Farjadnia, Mahsa
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    Fontan, Angela
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Russo, Alessio
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Molinari, Marco
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.
    What influences occupants' behavior in residential buildings: An experimental study on window operation in the KTH Live-In Lab2023In: 2023 IEEE Conference on Control Technology and Applications, CCTA 2023, 2023, p. 752-758Conference paper (Refereed)
    Abstract [en]

     Window-opening and window-closing behaviors play an important role in indoor environmental conditions and therefore have an impact on building energy efficiency. On the other hand, the same environmental conditions drive occupants to interact with windows. Understanding this mutual relationship of interaction between occupants and the residential building is thus crucial to improve energy efficiency without disregarding occupants' comfort. This paper investigates the influence of physical environmental variables (i.e., indoor and outside climate parameters) and categorical variables (i.e., time of the day) on occupants' behavior patterns related to window operation, utilizing a multivariate logistic regression analysis. The data considered in this study are collected during winter months, when the effect on the energy consumption of the window operation is the highest, at a Swedish residential building, the KTH Live-In Lab, accommodating four occupants in separate studio apartments. Although all the occupants seem to share a sensitivity to some common factors, such as air quality and time of the day, we can also observe individual variability with respect to the most significant drivers influencing window operation behaviors. 

  • 294.
    Farokhi, Farhad
    et al.
    CSIROs Data61, Canberra, ACT, Australia.; Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic, Australia..
    Sandberg, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Ensuring privacy with constrained additive noise by minimizing Fisher information2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 99, p. 275-288Article in journal (Refereed)
    Abstract [en]

    The problem of preserving the privacy of individual entries of a database when responding to linear or nonlinear queries with constrained additive noise is considered. For privacy protection, the response to the query is systematically corrupted with an additive random noise whose support is a subset or equal to a pre-defined constraint set. A measure of privacy using the inverse of the trace of the Fisher information matrix is developed. The Cramer-Rao bound relates the variance of any estimator of the database entries to the introduced privacy measure. The probability density that minimizes the trace of the Fisher information (as a proxy for maximizing the measure of privacy) is computed. An extension to dynamic problems is also presented. Finally, the results are compared to the differential privacy methodology. Crown Copyright

  • 295.
    Farokhi, Farhad
    et al.
    Univ Melbourne, Melbourne Informat Decis & Autonomous Syst Lab, Parkville, Vic 3010, Australia.;Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic 3010, Australia..
    Sandberg, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Fisher Information as a Measure of Privacy: Preserving Privacy of Households With Smart Meters Using Batteries2018In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 9, no 5, p. 4726-4734Article in journal (Refereed)
    Abstract [en]

    In this paper, batteries are used to preserve the privacy of households with smart meters. It is commonly understood that data from smart meters can be used by adversaries to infringe on the privacy of the households, e.g., figuring out the individual appliances that are being used or the level of the occupancy of the house. The Cramer-Rao bound is used to relate the variance of the estimation error of any unbiased estimator of the household consumption from the aggregate consumption (i.e., the household plus the battery) to the Fisher information. Subsequently, optimal policies for charging and utilizing batteries are devised to minimize the Fisher information (in the scalar case and the trace of the Fisher information matrix in the multi-variable case) as a proxy for maximizing the variance of the estimation error of the electricity consumption by adversaries (irrespective of their estimation policies). The policies are chosen to respect the physical constraints of the battery regarding capacity, initial charge, and rate constraints. The results are demonstrated on real power measurement data with non-intrusive load monitoring algorithms.

  • 296.
    Farokhi, Farhad
    et al.
    The University of Melbourne and CSIRO’s Data61, Melbourne, Australia bDepartment of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC, Australia cThe Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61, Canberra, Australia.
    Sandberg, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Fisher information privacy with application to smart meter privacy using HVAC units2019In: Privacy in Dynamical Systems, Springer Singapore , 2019, p. 3-17Chapter in book (Other academic)
    Abstract [en]

    In this chapter, we use Heating, Ventilation, and Air Conditioning (HVAC) units to preserve the privacy of households with smart meters in addition to regulating indoor temperature. We model the effect of the HVAC unit as an additive noise in the household consumption. The Cramér-Rao bound is used to relate the inverse of the trace of the Fisher information matrix to the quality of an adversary’s estimation error of the household private consumption from the aggregate consumption of the household with the HVAC unit. This establishes the Fisher information as the measure of privacy leakage. We compute the optimal privacy-preserving policy for controlling the HVAC unit through minimizing a weighted sum of the Fisher information and the cost operating the HVAC unit. The optimization problem also contains the constraints on the temperatures of the house. 

  • 297.
    Farokhi, Farhad
    et al.
    CSIROs Data61, Docklands, Australia.;Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic, Australia..
    Shames, Iman
    Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic, Australia..
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Private routing and ride-sharing using homomorphic encryption2020In: IET CYBER-PHYSICAL SYSTEMS: THEORY & APPLICATIONS, ISSN 2398-3396, Vol. 5, no 4, p. 311-320Article in journal (Refereed)
    Abstract [en]

    A framework for private and secure communication and interaction between agents interacting in transportation services is developed. An agent, i.e. a user, can ask questions or submit queries regarding whether the other agents, i.e. drivers, use the desired road at specific times of the day in an encrypted fashion. The authors developed the framework using semi-homomorphic encryption (namely, the Paillier's encryption method) to enable the algebraic manipulation of plain data without the need for decryption using appropriate computations over the encrypted data. Strong privacy and security guarantees are proved for the agents. Subsequently, the semi-homomorphic encryption method is utilised to develop privacy-aware ride-sharing and routing algorithms without the need for disclosing the origin and destination of the user.

  • 298.
    Fay, Dominik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Towards Scalable Machine Learning with Privacy Protection2023Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The increasing size and complexity of datasets have accelerated the development of machine learning models and exposed the need for more scalable solutions. This thesis explores challenges associated with large-scale machine learning under data privacy constraints. With the growth of machine learning models, traditional privacy methods such as data anonymization are becoming insufficient. Thus, we delve into alternative approaches, such as differential privacy.

    Our research addresses the following core areas in the context of scalable privacy-preserving machine learning: First, we examine the implications of data dimensionality on privacy for the application of medical image analysis. We extend the classification algorithm Private Aggregation of Teacher Ensembles (PATE) to deal with high-dimensional labels, and demonstrate that dimensionality reduction can be used to improve privacy. Second, we consider the impact of hyperparameter selection on privacy. Here, we propose a novel adaptive technique for hyperparameter selection in differentially gradient-based optimization. Third, we investigate sampling-based solutions to scale differentially private machine learning to dataset with a large number of records. We study the privacy-enhancing properties of importance sampling, highlighting that it can outperform uniform sub-sampling not only in terms of sample efficiency but also in terms of privacy.

    The three techniques developed in this thesis improve the scalability of machine learning while ensuring robust privacy protection, and aim to offer solutions for the effective and safe application of machine learning in large datasets.

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  • 299.
    Ferizbegovic, Mina
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Dual control concepts for linear dynamical systems2022Doctoral thesis, monograph (Other academic)
    Abstract [en]

    We study simultaneous learning and control of linear dynamical systems. In such a setting, control policies are derived with respect to two objectives: i) to control the system as well as possible, given the current knowledge of system dynamics (exploitation), and ii) to gather as much information as possible about the unknown system that can improve control (exploration).These two objectives are often in conflict, and this phenomenon is known as the exploration-exploitation trade-off.More specifically, in the context of simultaneous learning and control, we consider: linear quadratic regulation (LQR) problem, model reference control, and data-driven control based on Willems \textit{et al.}'s fundamental lemma. 

    First, we consider the LQR problem with unknown dynamics. We present robust and certainty equivalence (CE) model-based control methods that balance exploration and exploitation. We focus on control policies that can be iteratively updated after sequentially collecting data.

    We propose robust (with respect to parameter uncertainty) LQR design methods. To quantify uncertainty, we derive a methodbased on Bayesian inference, which is directly applicable to robust control synthesis. To begin, we derive a robust controller to minimize the worst-case cost, with high probability, given the empirical observation of the system. This robust controller synthesis is then used to derive a robust dual controller, which updates its control policy after collecting data. An episode in which data is collected is called exploration, and the episode using an updated control policy called exploitation. The objective is to minimize the worst-case cost of the updated control policy, requiring that a given exploration budget constrains the worst-case cost during exploration. Additionally, we derive methods that balance exploration and exploitation to minimize the cumulative worst-case cost for a fixed number of episodes. In this thesis, we refer to such a problem as robust reinforcement learning. Essentially, it is a robust dual controller aiming to minimize the cumulative worst-case cost, and that updates its control policy in each episode.Numerical experiments show that the proposed methods perform better than existing state-of-the-art algorithms. Moreover, experiments also indicate that the exploration prioritizes the uncertainty reduction in the parameters that matter most for control.

    A control policy using the CE principle for LQR consists of a sum of an optimal controller calculated using estimated dynamics at time $t$, and an additive external excitation.  It has been shown over the years that the optimal asymptotic rate of regret is in many instances $\mathcal{O}(\sqrt{T})$. In particular, this rate can be obtained by adding a white noise external excitation, with a variance decaying as $\gamma/\sqrt{T}$, where $\gamma$ is a predefined constant. As the amount of excitation is pre-determined, such approaches can be viewed as open-loop control of the external excitation.  In this thesis, we approach the problem of designing the external excitation from a feedback perspective leveraging the well-known benefits of feedback control for decreasing sensitivity to external disturbances and system-model mismatch, as compared to open-loop strategies. The benefits of this approach over the open-loop approach can be seen in the case of unmodeled dynamics and disturbances. However, even when using the benefits of feedback control, we do not calculate the optimal amount of external excitation. To find the optimal amount of external excitation, we suggest exploration strategies that are based on a time-dependent scaling $\gamma_t$ and can attain cumulative regret similar to or lower than cumulative regret obtained for optimal scaling $\gamma^*$ according to numerical examples.

    Second, we consider the model reference control problem with the goal of proposing a data-driven robust control design method based on an average risk criterion, which we call Bayes control. We show that this approach has very close ties to the Bayesian kernel-based method, but the conceptual difference lies in the use of a deterministic respective stochastic setting for the system parameters.  

    Finally, we consider data-driven control using Willems \textit{et al.}'s fundamental lemma. First, we propose variations of the fundamental lemma that, instead of a data trajectory, utilize correlation functions in the time domain, as well as power spectra of the input and the output in the frequency domain. Since data-driven control using the fundamental lemma can become a very expensive computation task for large datasets, the proposed variations are easy to computeeven for large datasets and can be efficient as a data compression technique. Second, we study connections of data informativity conditions between the results based on the fundamental lemma (finite time), and classical system identification. We show that finite time informativity conditions for state-space systems are closely linked to the identifiability conditions derived from the fundamental lemma. We prove that the obtained persistency of excitation conditions for infinite time are sufficient conditions for finite time informativity. Moreover, we reveal that the obtained conditions for a finite time in closed-loop are stricter than in classical system identification. This is a consequence of the noiseless data setting in the fundamental lemma that precludes the possibility of noise to excite the system in a feedback setting.

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  • 300.
    Ferizbegovic, Mina
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Robust learning and control of linear dynamical systems2020Licentiate thesis, monograph (Other academic)
    Abstract [en]

    We consider the linear quadratic regulation problem when the plant is an unknown linear dynamical system. We present robust model-based methods based on convex optimization, which minimize the worst-case cost with respect to uncertainty around model estimates. To quantify uncertainty, we derive a methodbased on Bayesian inference, which is directly applicable to robust control synthesis.We focus on control policies that can be iteratively updated after sequentially collecting data. More specifically, we seek to design control policies that balance exploration (reducing model uncertainty) and exploitation (control of the system) when exploration must be safe (robust).First, we derive a robust controller to minimize the worst-case cost, with high probability, given the empirical observation of the system. This robust controller synthesis is then used to derive a robust dual controller, which updates its control policy after collecting data. An episode in which data is collected is called exploration, and the episode using an updated control policy is exploitation. The objective is to minimize the worst-case cost of the updated control policy, requiring that a given exploration budget constrains the worst-case cost during exploration.We look into robust dual control in both finite and infinite horizon settings. The main difference between the finite and infinite horizon settings is that the latter does not consider the length of the exploration and exploitation phase, but it rather approximates the cost using the infinite horizon cost. In the finite horizon setting, we discuss how different exploration lengths affect the trade-off between exploration and exploitation.Additionally, we derive methods that balance exploration and exploitation to minimize the cumulative worst-case cost for a fixed number of episodes. In this thesis, we refer to such a problem as robust reinforcement learning. Essentially, it is a robust dual controller aiming to minimize the cumulative worst-case cost, and that updates its control policy in each episode.Numerical experiments show that the proposed methods have better performance compared to existing state-of-the-art algorithms. Moreover, experiments also indicate that the exploration prioritizes the uncertainty reduction in the parameters that matter most for control.

    Download full text (pdf)
    fulltext
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