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  • 101.
    Barros da Silva Jr., José Mairton
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Ntougias, Konstantinos
    University of Cyprus.
    Krikidis, Ioannis
    University of Cyprus.
    Fodor, Gabor
    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.
    Simultaneous Wireless Information and PowerTransfer for Federated Learning2021In: IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Lucca, Italy, Sep. 2021, IEEE Communications Society, 2021, p. 296-300Conference paper (Refereed)
    Abstract [en]

    In the Internet of Things, learning is one of most prominent tasks. In this paper, we consider an Internet of Things scenario where federated learning is used with simultaneous transmission of model data and wireless power. We investigate the trade-off between the number of communication rounds and communication round time while harvesting energy to compensate the energy expenditure. We formulate and solve an optimization problem by considering the number of local iterations on devices, the time to transmit-receive the model updates, and to harvest sufficient energy. Numerical results indicate that maximum ratio transmission and zero-forcing beamforming for the optimization of the local iterations on devices substantially boost the test accuracy of the learning task. Moreover, maximum ratio transmission instead of zero-forcing provides the best test accuracy and communication round time trade-off for various energy harvesting percentages. Thus, it is possible to learn a model quickly with few communication rounds without depleting the battery.

  • 102.
    Barros da Silva Jr., José Mairton
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Sabharwal, Ashutosh
    Fodor, Gabor
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Ericsson Res, S-16480 Stockholm, Sweden.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    1-bit Phase Shifters for Large-Antenna Full-Duplex mmWave Communications2020In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 19, no 10, p. 6916-6931Article in journal (Refereed)
    Abstract [en]

    Millimeter-wave using large-antenna arrays is a key technological component forthe future cellular systems, where it is expected that hybrid beamforming along withquantized phase shifters will be used due to their implementation and cost efficiency.In this paper, we investigate the efficacy of full-duplex mmWave communicationwith hybrid beamforming using low-resolution phase shifters, without any analogself-interference cancellation. We formulate the problem of joint self-interferencesuppression and downlink beamforming as a mixed-integer nonconvex joint opti-mization problem. We propose LowRes, a near-to-optimal solution using penaltydual decomposition. Numerical results indicate that LowRes using low-resolutionphase shifters perform within 3% of the optimal solution that uses infinite phaseshifter resolution. Moreover, even a single quantization bit outperforms half-duplextransmissions, respectively by 29% and 10% for both low and high residual self-interference scenarios, and for a wide range of practical antenna to radio-chain ratios.Thus, we conclude that 1-bit phase shifters suffice for full-duplex millimeter-wavecommunications, without requiring any additional new analog hardware.

  • 103.
    Barros da Silva Jr., José Mairton
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Sabharwal, Ashutosh
    Rice Univ, Houston, TX USA..
    Fodor, Gabor
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Ericsson Res, Kista, Sweden..
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Low Resolution Phase Shifters Suffice for Full-Duplex mmWave Communications2019In: 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS (ICC WORKSHOPS), IEEE , 2019Conference paper (Refereed)
    Abstract [en]

    Full-duplex base-stations with half-duplex nodes, allowing simultaneous uplink and downlink from different nodes, have the potential to double the spectrum efficiency without adding additional complexity at mobile nodes. Hybrid beam forming is commonly used in millimeter wave systems for its implementation efficiency. An important element of hybrid beam-forming is quantized phase shifters. In this paper, we ask if low-resolution phase shifters suffice for beamforming-based full-duplex millimeter wave systems. We formulate the problem of joint design for both self-interference suppression and downlink beamforming as an optimization problem, which we solve using penalty dual decomposition to obtain a near-optimal solution. Numerical results indicate that low-resolution phase shifters can perform close to systems that use infinite phase shifter resolution, and that even a single quantization bit outperforms half-duplex transmissions in both low and high residual self-interference scenarios.

  • 104.
    Basati, Amir
    et al.
    Center for Research on Microgrids (CROM), AAU Energy, Aalborg University.
    Guerrero, Josep M.
    Center for Research on Microgrids (CROM), AAU Energy, Aalborg University.
    Vasquez, Juan C.
    Center for Research on Microgrids (CROM), AAU Energy, Aalborg University.
    Fakharian, Ahmad
    Department of Electrical Engineering, Qazvin Islamic Azad University (QIAU).
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Golestan, Saeed
    Center for Research on Microgrids (CROM), AAU Energy, Aalborg University.
    Robust internal model-based voltage control for DC microgrids: An LMI based H∞ control2023In: Sustainable Energy, Grids and Networks, E-ISSN 2352-4677, Vol. 35, article id 101094Article in journal (Refereed)
    Abstract [en]

    This paper aims to design a robust internal model-based voltage control (RIMVC) scheme for DC Microgrids (DCMGs) in the presence of unknown external disturbances. Maintaining voltage reference tracking under measurement noise, delays, model parameter uncertainties and unknown external disturbances while the load changes simultaneously is a severe challenge for the DC–DC converters in DCMGs. By developing a modified internal model-based voltage control for DC–DC converters, this work proposes a plug-and-play (PnP) robust voltage control scheme to address the abovementioned challenge. The proposed control method has a cascade structure. In the first step, a modified IMC control is designed to achieve desired tracking performance for a nominal dynamical system. In the next step, the output feedback H∞ control part is added to improve the performance robustness under external disturbances and parameter uncertainties. The efficiency of the proposed control scheme is evaluated using a real-time MATLAB/Simulink testbed, taking into account unknown internal and external disturbances under various rapid voltage reference changes, model parameter uncertainties, constant power loads, system delays and normal load profile changes in multiple case study scenarios.

  • 105.
    Bassi, Germán
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
    Nekouei, Ehsan
    City University of Hong KongKowloon TongHong Kong.
    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).
    Statistical Parameter Privacy2020In: Privacy in Dynamical Systems / [ed] Farhad Farokhi, Springer Nature, 2020, p. 65-82Chapter in book (Refereed)
    Abstract [en]

    We investigate the problem of sharing the outcomes of a parametric source with an untrusted party while ensuring the privacy of the parameters. We propose privacy mechanisms which guarantee parameter privacy under both Bayesian statistical as well as information-theoretic privacy measures. The properties of the proposed mechanisms are investigated and the utility-privacy trade-off is analyzed.

  • 106.
    Bastianello, Nicola
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Carli, Ruggero
    Department of Information Engineering (DEI), University of Padova, Italy.
    Simonetto, Andrea
    UMA, ENSTA Paris, Institut Polytechnique de Paris, Palaiseau 91120, France.
    Extrapolation-Based Prediction-Correction Methods for Time-varying Convex Optimization2023In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 210, article id 109089Article in journal (Refereed)
    Abstract [en]

    In this paper, we focus on the solution of online optimization problems that arise often in signal processing and machine learning, in which we have access to streaming sources of data. We discuss algorithms for online optimization based on the prediction-correction paradigm, both in the primal and dual space. In particular, we leverage the typical regularized least-squares structure appearing in many signal processing problems to propose a novel and tailored prediction strategy, which we call extrapolation-based. By using tools from operator theory, we then analyze the convergence of the proposed methods as applied both to primal and dual problems, deriving an explicit bound for the tracking error, that is, the distance from the time-varying optimal solution. We further discuss the empirical performance of the algorithm when applied to signal processing, machine learning, and robotics problems.

  • 107.
    Bastianello, Nicola
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Carli, Ruggero
    University of Padova, Department of Information Engineering (DEI), Padova, Italy, 35131.
    Zampieri, Sandro
    University of Padova, Department of Information Engineering (DEI), Padova, Italy, 35131.
    Internal Model-Based Online Optimization2024In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 69, no 1, p. 689-696Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a model-based approach to the design of online optimization algorithms, with the goal of improving the tracking of the solution trajectory (trajectories) w.r.t. state-of-the-art methods. We focus first on quadratic problems with a time-varying linear term, and use digital control tools (a robust internal model principle) to propose a novel online algorithm that can achieve zero tracking error by modeling the cost with a dynamical system. We prove the convergence of the algorithm for both strongly convex and convex problems. We further discuss the sensitivity of the proposed method to model uncertainties and quantify its performance. We discuss how the proposed algorithm can be applied to general (nonquadratic) problems using an approximate model of the cost, and analyze the convergence leveraging the small gain theorem. We present numerical results that showcase the superior performance of the proposed algorithms over previous methods for both quadratic and nonquadratic problems.

  • 108.
    Bastianello, Nicola
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Rikos, Apostolos I.
    Boston University, Division of Systems Engineering, Department of Electrical and Computer Engineering, Boston, MA, US.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Online Distributed Learning with Quantized Finite-Time Coordination2023In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 5026-5032Conference paper (Refereed)
    Abstract [en]

    In this paper we consider online distributed learning problems. Online distributed learning refers to the process of training learning models on distributed data sources. In our setting a set of agents need to cooperatively train a learning model from streaming data. Differently from federated learning, the proposed approach does not rely on a central server but only on peer-to-peer communications among the agents. This approach is often used in scenarios where data cannot be moved to a centralized location due to privacy, security, or cost reasons. In order to overcome the absence of a central server, we propose a distributed algorithm that relies on a quantized, finite-time coordination protocol to aggregate the locally trained models. Furthermore, our algorithm allows for the use of stochastic gradients during local training. Stochastic gradients are computed using a randomly sampled subset of the local training data, which makes the proposed algorithm more efficient and scalable than traditional gradient descent. In our paper, we analyze the performance of the proposed algorithm in terms of the mean distance from the online solution. Finally, we present numerical results for a logistic regression task.

  • 109.
    Bauer, Stefan
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Redmond, Stephen J.
    University College Dublin, University College Dublin.
    et al.,
    Real Robot Challenge: A Robotics Competition in the Cloud2022In: Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, ML Research Press , 2022, p. 190-204Conference paper (Refereed)
    Abstract [en]

    Dexterous manipulation remains an open problem in robotics. To coordinate efforts of the research community towards tackling this problem, we propose a shared benchmark. We designed and built robotic platforms that are hosted at the MPI-IS1 and can be accessed remotely. Each platform consists of three robotic fingers that are capable of dexterous object manipulation. Users are able to control the platforms remotely by submitting code that is executed automatically, akin to a computational cluster. Using this setup, i) we host robotics competitions, where teams from anywhere in the world access our platforms to tackle challenging tasks ii) we publish the datasets collected during these competitions (consisting of hundreds of robot hours), and iii) we give researchers access to these platforms for their own projects.

  • 110.
    Baumann, Dominik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Max Planck Institute for Intelligent Systems.
    Fast and Resource-Efficient Control of Wireless Cyber-Physical Systems2019Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Cyber-physical systems (CPSs) tightly integrate physical processes with computing and communication to autonomously interact with the surrounding environment.This enables emerging applications such as autonomous driving, coordinated flightof swarms of drones, or smart factories. However, current technology does notprovide the reliability and flexibility to realize those applications. Challenges arisefrom wireless communication between the agents and from the complexity of thesystem dynamics. In this thesis, we take on these challenges and present three maincontributions.We first consider imperfections inherent in wireless networks, such as communication delays and message losses, through a tight co-design. We tame the imperfectionsto the extent possible and address the remaining uncertainties with a suitable controldesign. That way, we can guarantee stability of the overall system and demonstratefeedback control over a wireless multi-hop network at update rates of 20-50 ms.If multiple agents use the same wireless network in a wireless CPS, limitedbandwidth is a particular challenge. In our second contribution, we present aframework that allows agents to predict their future communication needs. Thisallows the network to schedule resources to agents that are in need of communication.In this way, the limited resource communication can be used in an efficient manner.As a third contribution, to increase the flexibility of designs, we introduce machinelearning techniques. We present two different approaches. In the first approach,we enable systems to automatically learn their system dynamics in case the truedynamics diverge from the available model. Thus, we get rid of the assumption ofhaving an accurate system model available for all agents. In the second approach, wepropose a framework to directly learn actuation strategies that respect bandwidthconstraints. Such approaches are completely independent of a system model andstraightforwardly extend to nonlinear settings. Therefore, they are also suitable forapplications with complex system dynamics.

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  • 111.
    Baumann, Dominik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Max Planck Institute for Intelligent Systems.
    Learning and Control Strategies for Cyber-physical Systems: From Wireless Control over Deep Reinforcement Learning to Causal Identification2020Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Cyber-physical systems (CPS) integrate physical processes with computing and communication to autonomously interact with the environment. This enables emerging applications such as autonomous driving or smart factories. However, current technology does not provide the dependability and adaptability to realize those applications. CPS are systems with complex dynamics that need to be adaptive, communicate with each other over wireless channels, and provide theoretical guarantees on proper functioning. In this thesis, we take on the challenges imposed by wireless CPS by developing appropriate learning and control strategies.

    In the first part of the thesis, we present a holistic approach that enables provably stable feedback control over wireless networks. At design time (i.e., prior to execution), we tame typical imperfections inherent in wireless networks, such as communication delays and message loss. The remaining imperfections are then accounted for through feedback control. At run time (i.e., during execution), we let systems reason about communication demands and allocate communication resources accordingly. We provide theoretical stability guarantees and evaluate the approach on a cyber-physical testbed, featuring a multi-hop wireless network supporting multiple cart-pole systems.

    In the second part, we enhance the flexibility of our designs through learning. We first propose a framework based on deep reinforcement learning to jointly learn control and communication strategies for wireless CPS by integrating both objectives, control performance and saving communication resources, in the reward function. This enables learning of resource-aware controllers for nonlinear and high-dimensional systems. Second, we propose an approach for evaluating the performance of models of wireless CPS through online statistical analysis. We trigger learning in case performance drops, that way limiting the number of learning experiments and reducing computational complexity. Third, we propose an algorithm for identifying the causal structure of control systems. We provide theoretical guarantees on learning the true causal structure and demonstrate enhanced generalization capabilities inherited through causal structure identification on a real robotic system.

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  • 112.
    Baurnann, Dominik
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Max Planck Institute for Intelligent Systems, Stuttgart/Tübingen, Germany.
    Solowjow, F.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Trimpe, S.
    Event-triggered pulse control with model learning (if Necessary)2019In: Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 792-797, article id 8814333Conference paper (Refereed)
    Abstract [en]

    In networked control systems, communication is a shared and therefore scarce resource. Event-triggered control (ETC) can achieve high performance control with a significantly reduced amount of samples compared to classical, periodic control schemes. However, ETC methods usually rely on the availability of an accurate dynamics model, which is oftentimes not readily available. In this paper, we propose a novel event-triggered pulse control strategy that learns dynamics models if necessary. In addition to adapting to changing dynamics, the method also represents a suitable replacement for the integral part typically used in periodic control.

  • 113.
    Bechlioulis, C. P.
    et al.
    School of Mechanical Engineering, Control Systems Lab, National Technical University of Athens, Athens, Greece.
    Heshmati Alamdari, Shahabodin
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Karras, G. C.
    School of Mechanical Engineering, Control Systems Lab, National Technical University of Athens, Athens, Greece.
    Marantos, P.
    School of Mechanical Engineering, Control Systems Lab, National Technical University of Athens, Athens, Greece.
    Kyriakopoulos, K. J.
    School of Mechanical Engineering, Control Systems Lab, National Technical University of Athens, Athens, Greece.
    Sensor-based motion control of autonomous underwater vehicles, part II: Robust motion control strategies2020In: Autonomous Underwater Vehicles, Institution of Engineering and Technology (IET) , 2020, p. 45-78Chapter in book (Other academic)
    Abstract [en]

    The first section of this chapter presents an NMPC strategy for underwater robotic vehicles operating under various constraints. The purpose of the controller is to guide the vehicle towards specific way -points. Various constraints such as obstacles, workspace boundaries and control input saturation as well as predefined upper bound of the vehicle velocity (requirements for several underwater tasks such as seabed inspection scenario and mosaicking) are considered during the control design. The proposed scheme incorporates the full dynamics of the vehicle in which the ocean currents are also involved. The controller is designed in order to find the optimal thrusts required for minimizing the way -point tracking error. Moreover, the controlinputs calculated by the proposed approach are formulated in a way that the vehicle will exploit the ocean currents, when they are in favor of the way -point tracking mission, which results in reduced energy consumption by the thrusters. In the second part of this chapter, novel position- and trajectory -tracking control schemes for AUVs are presented. The proposed controllers do not utilize the vehicle’s dynamic model parameters and guarantee prescribed transient and steady-state performance despite the presence of external disturbances and kinematic constraints for the case of underactuated vehicles. Moreover, through the appropriate selection of certain performance functions, the proposed scheme can also guarantee the satisfaction of motion and performance constraints imposed by the desired task.

  • 114.
    Beerens, R.
    et al.
    Eindhoven University of Technology, Department of Mechanical Engineering, Eindhoven, 5600, MB, Netherlands.
    Bisoffi, Andrea
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Zaccarian, L.
    LAAS, Univ. de Toulouse, LAAS, CNRS, Toulouse, F-31400, France.
    Heemels, W. P. M. H.
    Eindhoven University of Technology, Department of Mechanical Engineering, Eindhoven, 5600, MB, Netherlands.
    Nijmeijer, H.
    Eindhoven University of Technology, Department of Mechanical Engineering, Eindhoven, 5600, MB, Netherlands.
    Van De Wouw, N.
    Eindhoven University of Technology, Department of Mechanical Engineering, Eindhoven, 5600, MB, Netherlands; Department of Civil, Environmental and GeoEngineering, University of Minnesota, Minneapolis, 55455, MN, United States..
    Hybrid PID control for transient performance improvement of motion systems with friction2018In: 2018 Annual American Control Conference (ACC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 539-544, article id 8431613Conference paper (Refereed)
    Abstract [en]

    We present a novel reset control approach to improve transient performance of a PID-controlled motion system subject to friction. In particular, a reset integrator is applied to circumvent the depletion and refilling process of a linear integrator when the system overshoots the setpoint, thereby significantly reducing settling times. Moreover, robustness for unknown static friction levels is obtained. A hybrid closed-loop system formulation is derived, and stability follows from a discontinuous Lyapunov-like function and a meagre-limsup invariance argument. The working principle of the controller is illustrated by means of a numerical example.

  • 115.
    Beerens, R.
    et al.
    Eindhoven Univ Technol, Dept Mech Engn, NL-5600 MB Eindhoven, Netherlands..
    Bisoffi, Andrea
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Zaccarian, L.
    Univ Toulouse, LAAS, CNRS, F-31400 Toulouse, France.;Univ Trento, I-38122 Trento, Italy..
    Heemels, W. P. M. H.
    Eindhoven Univ Technol, Dept Mech Engn, NL-5600 MB Eindhoven, Netherlands..
    Nijmeijer, H.
    Eindhoven Univ Technol, Dept Mech Engn, NL-5600 MB Eindhoven, Netherlands..
    van de Wouw, N.
    Eindhoven Univ Technol, Dept Mech Engn, NL-5600 MB Eindhoven, Netherlands.;Univ Minnesota, Civil Environm & Geoengn Dept, Minneapolis, MN 55455 USA..
    Reset integral control for improved settling of PID-based motion systems with friction2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 107, p. 483-492Article in journal (Refereed)
    Abstract [en]

    We present a reset control approach to improve the transient performance of a PID-controlled motion system subject to Coulomb and viscous friction. A reset integrator is applied to circumvent the depletion and refilling process of a linear integrator when the solution overshoots the setpoint, thereby significantly reducing the settling time. Robustness for unknown static friction levels is obtained. The closed-loop system is formulated through a hybrid systems framework, within which stability is proven using a discontinuous Lyapunov-like function and a meagre-limsup invariance argument. The working principle of the proposed reset controller is analyzed in an experimental benchmark study of an industrial high-precision positioning machine.

  • 116.
    Bereza-Jarocinski, Robert
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Eriksson, Oscar
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Abdalmoaty, Mohamed R-H
    Uppsala Univ, Div Syst & Control, S-75105 Uppsala, Sweden..
    Broman, David
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Stochastic Approximation for Identification of Non-Linear Differential-Algebraic Equations with Process Disturbances2022In: 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 6712-6717Conference paper (Refereed)
    Abstract [en]

    Differential-algebraic equations, commonly used to model physical systems, are the basis for many equation-based object-oriented modeling languages. When systems described by such equations are influenced by unknown process disturbances, estimating unknown parameters from experimental data becomes difficult. This is because of problems with the existence of well-defined solutions and the computational tractability of estimators. In this paper, we propose a way to minimize a cost function-whose minimizer is a consistent estimator of the true parameters-using stochastic gradient descent. This approach scales significantly better with the number of unknown parameters than other currently available methods for the same type of problem. The performance of the method is demonstrated through a simulation study with three unknown parameters. The experiments show a significantly reduced variance of the estimator, compared to an output error method neglecting the influence of process disturbances, as well as an ability to reduce the estimation bias of parameters that the output error method particularly struggles with.

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  • 117.
    Bereza-Jarocinski, Robert
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Persson, Linnea
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Wahlberg, Bo
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Linköping Univ, Div Automat Control, S-58183 Linköping, Sweden..
    Distributed Model Predictive Control for Cooperative Landing2020In: Proceedings 21st IFAC World Congress on Automatic Control - Meeting Societal Challenges, Elsevier BV , 2020, Vol. 53, no 2, p. 15180-15185Conference paper (Refereed)
    Abstract [en]

    We design, implement and test two control algorithms for autonomously landing a drone on an autonomous boat. The first algorithm uses distributed model predictive control (DMPC), while the second combines a cascade controller with DMPC. The algorithms are implemented on a real drone, while the boat's motion is simulated, and their performance is compared to a centralized model predictive controller. Field experiments are performed, where all algorithms show an ability to land while avoiding violation of the safety constraints. The two distributed algorithms further show the ability to use longer prediction horizons than the centralized model predictive controller, especially in the cascade case, and also demonstrate improved robustness towards breaks in communication.

  • 118.
    Berglund, Erik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Novel Hessian approximations in optimization algorithms2022Licentiate thesis, monograph (Other academic)
    Abstract [en]

    There are several benefits of taking the Hessian of the objective function into account when designing optimization algorithms. Compared to using strictly gradient-based algorithms, Hessian-based algorithms usually require fewer iterations to converge. They are generally less sensitive to tuning of parameters and can better handle ill-conditioned problems. Yet, they are not universally used, due to there being several challenges associated with adapting them to various challenging settings. This thesis deals with Hessian-based optimization algorithms for large-scale, distributed and zeroth-order problems. For the large-scale setting, we contribute with a new way of deriving limited memory quasi-Newton methods, which we show can achieve better results than traditional limited memory quasi-Newton methods with less memory for some logistic and linear regression problems. For the distributed setting, we perform an analysis of how the error of a Newton-step is affected by the condition number and the number of iterations of a consensus-algorithm based on averaging, We show that the number of iterations needed to solve a quadratic problem with relative error less than ε grows logarithmically with 1/ε and also with the condition number of the Hessian of the centralized problem. For the zeroth order setting, we exploit the fact that a finite difference estimate of the directional derivative works as an approximate sketching technique, and use this to propose a zeroth order extension of a sketched Newton method that has been developed to solve large-scale problems. With the extension of this method to the zeroth order setting, we address the combined challenge of large-scale and zeroth order problems.

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  • 119.
    Berglund, Erik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Novel Hessian-Based Algorithms for Unconstrained Optimization2024Doctoral thesis, monograph (Other academic)
    Abstract [en]

    There are several benefits of taking the Hessian of the objective function into account when designing optimization algorithms. Compared to using strictly gradient-based algorithms, Hessian-based algorithms usually require fewer iterations to converge. They are generally less sensitive to tuning of parameters and can better handle ill-conditioned problems. Yet, they are not universally used, due to there being several challenges associated with adapting them to various challenging settings. This thesis deals with Hessian-based optimization algorithms for large-scale, stochastic, distributed, and zeroth-order problems. For the large-scale setting, we contribute with a new way of deriving limited memory quasi-Newton methods, which we show can achieve better results than traditional limited memory quasi-Newton methods with less memory for some logistic and linear regression problems. For the stochastic setting, we relax the secant condition used in traditional quasi-Newton methods and derive a novel quasi-Newton update that always preserves positive definiteness. Based on this, we develop an algorithm that exhibits linear convergence toward a neighborhood of the optimal solution, even if gradient and function evaluations are subject to bounded perturbations. For the distributed setting, we contribute with two different projects. Firstly, we perform an analysis of how the error of a Newton-step is affected by the condition number and the number of iterations of a consensus-algorithm based on averaging. We show that the number of iterations needed to solve a quadratic problem with relative error less than ε grows logarithmically with 1/ε and also with the condition number of the Hessian of the centralized problem. Secondly, we consider how Hessians and Hessian approximations can be used to compensate for communication delays in asynchronous implementations of the Incremental Aggregated Gradient (IAG) algorithm. We provide a general convergence theorem that can be used to analyze delay compensation using various Hessian approximations, apply it to the previously proposed Curvature-Aided IAG (CIAG), and propose delay compensation with some cheaper Hessian approximations that nevertheless outperform IAG without delay compensation. For the zeroth order setting, we exploit the fact that a finite difference estimate of the directional derivative works as an approximate sketching technique and use this to propose a zeroth order extension of a sketched Newton method that has been developed to solve large-scale problems. With the extension of this method to the zeroth order setting, we address the combined challenge of large-scale and zeroth order problems. 

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  • 120.
    Berglund, Erik
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Khirirat, Sarit
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Wang, Xiaoyu
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Zeroth-order randomized subspace newton methods2022In: 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 6002-6006Conference paper (Refereed)
    Abstract [en]

    Zeroth-order methods have become important tools for solving problems where we have access only to function evaluations. However, the zeroth-order methods only using gradient approximations are n times slower than classical first-order methods for solving n-dimensional problems. To accelerate the convergence rate, this paper proposes the zeroth order randomized subspace Newton (ZO-RSN) method, which estimates projections of the gradient and Hessian by random sketching and finite differences. This allows us to compute the Newton step in a lower dimensional subspace, with small computational costs. We prove that ZO-RSN can attain lower iteration complexity than existing zeroth order methods for strongly convex problems. Our numerical experiments show that ZO-RSN can perform black-box attacks under a more restrictive limit on the number of function queries than the state-of-the-art Hessian-aware zeroth-order method.

  • 121.
    Berglund, Erik
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Khirirat, Sarit
    Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE.
    Wu, Xuyang
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Magnússon, Sindri
    Department of Computer and System Science, Stockholm University, Stockholm, Sweden.
    Johansson, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Revisiting the Curvature-aided IAG: Improved Theory and Reduced Complexity2023In: IFAC-PapersOnLine, Elsevier BV , 2023, Vol. 56, p. 5221-5226Conference paper (Refereed)
    Abstract [en]

    The curvature-aided IAG (CIAG) algorithm is an efficient asynchronous optimization method that accelerates IAG using a delay compensation technique. However, existing step-size rules for CIAG are conservative and hard to implement, and the Hessian computation in CIAG is often computationally expensive. To alleviate these issues, we first provide an easy-to-implement and less conservative step-size rule for CIAG. Next, we propose a modified CIAG algorithm that reduces the computational complexity by approximating the Hessian with a constant matrix. Convergence results are derived for each algorithm on both convex and strongly convex problems, and numerical experiments on logistic regression demonstrate their effectiveness in practice.

  • 122.
    Berglund, Erik
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Magnússon, Sindri
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Johansson, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Distributed Newton Method Over Graphs: Can Sharing of Second-Order Information Eliminate the Condition Number Dependence?2021In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 28, p. 1180-1184Article in journal (Refereed)
    Abstract [en]

    One of the main advantages of second-order methods in a centralized setting is that they are insensitive to the condition number of the objective function's Hessian. For applications such as regression analysis, this means that less pre-processing of the data is required for the algorithm to work well, as the ill-conditioning caused by highly correlated variables will not be as problematic. Similar condition number independence has not yet been established for distributed methods. In this paper, we analyze the performance of a simple distributed second-order algorithm on quadratic problems and show that its convergence depends only logarithmically on the condition number. Our empirical results indicate that the use of second-order information can yield large efficiency improvements over first-order methods, both in terms of iterations and communications, when the condition number is of the same order of magnitude as the problem dimension.

  • 123.
    Berkane, Soulaimaine
    et al.
    Department of Computer Science and Engineering, University of Quebec in Outaouais, 101 St-Jean Bosco, Gatineau, QC, J8X 3X7, Canada.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Constrained stabilization on the n-sphere2021In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 125, article id 109416Article in journal (Refereed)
    Abstract [en]

    We solve the stabilization problem on the n-sphere in the presence of conic constraints. We use the stereographic projection to map this problem to the classical navigation problem on Rn in the presence of spherical obstacles. As a consequence, any obstacle avoidance algorithm for navigation in the Euclidean space can be used to solve the given problem on the n-sphere. We illustrate the effectiveness of the approach using the kinematics of the reduced attitude model on the 2-sphere. 

  • 124.
    Berkane, Soulaimane
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Bisoffi, Andrea
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    A hybrid controller for obstacle avoidance in an n-dimensional euclidean space2019In: 2019 18th European Control Conference, ECC 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 764-769, article id 8795713Conference paper (Refereed)
    Abstract [en]

    For a vehicle moving in an n-dimensional Euclidean space, we present a construction of a hybrid feedback that guarantees both global asymptotic stabilization of a reference position and avoidance of an obstacle corresponding to a bounded spherical region. The proposed hybrid control algorithm switches between two modes of operation: stabilization (motion-to-goal) and avoidance (boundary-following). The geometric construction of the flow and jump sets of the hybrid controller, exploiting a hysteresis region, guarantees robust switching (chattering-free) between stabilization and avoidance. Simulation results illustrate the performance of the proposed hybrid control approach for a 3-dimensional scenario.

  • 125.
    Berkane, Soulaimane
    et al.
    Univ Quebec Outaouais, Dept Comp Sci & Engn, Gatineau, PQ J8X 3X7, Canada..
    Bisoffi, Andrea
    Univ Groningen, ENTEG, NL-9747 AG Groningen, Netherlands.;Univ Groningen, JC Willems Ctr Syst & Control, NL-9747 AG Groningen, Netherlands..
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Obstacle Avoidance via Hybrid Feedback2022In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 67, no 1, p. 512-519Article in journal (Refereed)
    Abstract [en]

    In this article, we present a hybrid feedback approach to solve the navigation problem in the n-dimensional space containing an arbitrary number of ellipsoidal obstacles. The proposed algorithm guarantees both global asymptotic stabilization to a target position and avoidance of the obstacles. The controller, exploiting hysteresis regions, employs a Zeno-free switching between two modes of control: stabilization and avoidance. Simulation results illustrate the performance of the proposed approach for 2-D and 3-D scenarios.

  • 126.
    Berkane, Soulaimane
    et al.
    Department of Computer Science and Engineering, University of Quebec in Outaouais, 101 St-Jean Bosco, Gatineau, QC, J8X 3X7, Canada, 101 St-Jean Bosco.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Reciprocal Safety Velocity Cones for Decentralized Collision Avoidance in Multi-Agent Systems2023In: 22nd IFAC World CongressYokohama, Japan, July 9-14, 2023, Elsevier BV , 2023, Vol. 56, p. 8024-8029Conference paper (Refereed)
    Abstract [en]

    In this paper, we solve the inter-agent collision avoidance problem in an arbitrary n−dimensional Euclidean space using reciprocal safety velocity cones (RSVCs). We propose a decentralized feedback control strategy that guarantees simultaneously asymptotic stabilization to a reference and collision avoidance. Our algorithm is purely decentralized in the sense that each agent uses only local information about its neighbouring agents. Moreover, the proposed solution can be implemented using only inter-agent bearing measurements. Therefore, the algorithm is a sensor-based control strategy which is practically implementable using a wide range of sensors such as vision systems and range scanners. Simulation results in a two dimensional environment cluttered with agents shows that the number of possible deadlocks is marginal and decrease with the decrease in the clutteredness of the workspace.

  • 127.
    Berkane, Soulaimane
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Tayebi, A.
    Attitude estimation with intermittent measurements2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 105, p. 415-421Article in journal (Refereed)
    Abstract [en]

    We propose a framework for attitude estimation on the Special Orthogonal group SO(3) using intermittent body-frame vector measurements. We consider the case where the vector measurements are synchronously-intermittent (all measurements are received at the same time) and the case where the vector measurements are asynchronously-intermittent (not all measurements are received at the same time). The proposed observers have a measurement-triggered structure where the attitude is predicted using the continuously measured angular velocity when the vector measurements are not available, and adequately corrected upon the arrival of the vector measurements. A hybrid framework is proposed to capture the behaviour of the closed-loop system by extending the state with timers that are reset at each jump of the observer state. Almost global asymptotic stability is shown using rigorous Lyapunov techniques for hybrid systems.

  • 128.
    Berkane, Soulaimane
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Tayebi, Abdelhamid
    Univ Western Ontario, Dept Elect & Comp Engn, London, ON, Canada.;Lakehead Univ, Dept Elect Engn, Thunder Bay, ON, Canada..
    Teel, Andrew R.
    Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA..
    Hybrid Constrained Estimation For Linear Time-Varying Systems2018In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2018, p. 4643-4648Conference paper (Refereed)
    Abstract [en]

    For linear time-varying systems with possibly constrained states, we propose a hybrid observer that guarantees the containment of the estimated state variables in a prescribed domain of interest. The hybrid observer employs a Kalmantype continuous estimator during the flows while, during the jumps, projects the state estimates onto the set described by the constraint equation. A suitable choice of the flow and jump sets allows to conclude uniform global asymptotic stability of the zero estimation error set.

  • 129.
    Berkane, Soulaimane
    et al.
    Univ Quebec Outaouis, Dept Informat & Ingn, Gatineau, PQ J8X 3X7, Canada.;Lakehead Univ, Dept Elect Engn, Thunder Bay, ON P7B 5E1, Canada..
    Theodosis, Dionysis
    Tech Univ Crete, Dynam Syst & Simulat Lab, Khania 73100, Greece.;Inst Univ France, F-06903 Sophia Antipolis, France..
    Hamel, Tarek
    Univ Cote Azur, I3S CNRS, F-06903 Sophia Antipolis, France..
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    State Estimation for Linear Systems With Quadratic Outputs2023In: IEEE Control Systems Letters, E-ISSN 2475-1456, Vol. 7, p. 3872-3877Article in journal (Refereed)
    Abstract [en]

    This letter deals with the problem of state estimation for a class of systems involving linear dynamics with multiple quadratic output measurements. We propose a systematic approach to immerse the original system into a linear time-varying (LTV) system of a higher dimension. The methodology extends the original system by incorporating a minimum number of auxiliary states, ensuring that the resulting extended system exhibits both linear dynamics and linear output. Consequently, any Kalman-type observer can showcase global state estimation, provided the system is uniformly observable.

  • 130.
    Bernardo, Carmela
    et al.
    Division of Automatic Control, Department of Electrical Engineering, Linköping University, SE-58183 Linköping, Sweden.
    Wang, Lingfei
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Fridahl, Mathias
    Department of Thematic Studies - Environmental Change, Linköping University, SE-58183 Linköping, Sweden.
    Altafini, Claudio
    Division of Automatic Control, Department of Electrical Engineering, Linköping University, SE-58183 Linköping, Sweden.
    Quantifying leadership in climate negotiations: A social power game2023In: PNAS Nexus, E-ISSN 2752-6542, Vol. 2, no 11, article id pgad365Article in journal (Refereed)
    Abstract [en]

    We consider complex multistage multiagent negotiation processes such as those occurring at climate conferences and ask ourselves how can an agent maximize its social power, intended as influence over the outcome of the negotiation. This question can be framed as a strategic game played over an opinion dynamics model, in which the action of an agent consists in stubbornly defending its own opinion. We show that for consensus-seeking opinion dynamics models in which the interaction weights are uniform, the optimal action obeys to an early mover advantage principle, i.e. the agents behaving stubbornly in the early phases of the negotiations achieve the highest social power. When looking at data collected from the climate change negotiations going on at the United Nations Framework Convention on Climate Change, we find evidence of the use of the early mover strategy. Furthermore, we show that the social powers computed through our model correlate very well with the perceived leadership roles assessed through independent survey data, especially when non-uniform weights incorporating economical and demographic factors are considered.

  • 131.
    Besselink, Bart
    et al.
    Univ Groningen, Jan C Willems Ctr Syst & Control, Groningen, Netherlands.;Univ Groningen, Bernoulli Inst Math Comp Sci & Artificial Intelli, Groningen, Netherlands..
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    van der Schaft, Arjan
    Univ Groningen, Jan C Willems Ctr Syst & Control, Groningen, Netherlands.;Univ Groningen, Bernoulli Inst Math Comp Sci & Artificial Intelli, Groningen, Netherlands..
    Contracts as specifications for dynamical systems in driving variable form2019In: 2019 18TH EUROPEAN CONTROL CONFERENCE (ECC), IEEE , 2019, p. 263-268Conference paper (Refereed)
    Abstract [en]

    This paper introduces assume/guarantee contracts on continuous-time control systems, hereby extending contract theories for discrete systems to certain new model classes and specifications. Contracts are regarded as formal characterizations of control specifications, providing an alternative to specifications in terms of dissipativity properties or set-invariance. The framework has the potential to capture a richer class of specifications more suitable for complex engineering systems. The proposed contracts are supported by results that enable the verification of contract implementation and the comparison of contracts. These results are illustrated by an example of a vehicle following system.

  • 132.
    Bhat, Sriharsha
    et al.
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics.
    Panteli, Chariklia
    KTH, School of Engineering Sciences (SCI).
    Stenius, Ivan
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV2023In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 40, no 7, p. 1840-1859Article in journal (Refereed)
    Abstract [en]

    Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under-ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV-MPC) is derived from the nonlinear model to enable real-time control. In simulations, NMPC and LTV-MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise-time, overshoot, steady-state error, and robustness. The LTV-MPC shows satisfactory real-time performance in experimental validation. The paper further also demonstrates experimentally that LTV-MPC can be run real-time on the AUV in performing hydrobatic maneouvers.

  • 133.
    Biel, Martin
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Distributed Stochastic Programming with Applications to Large-Scale Hydropower Operations2021Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Stochastic programming is a subfield of mathematical programming concerned with optimization problems subjected to uncertainty. Many engineering problems with random elements can be accurately modeled as a stochastic program. In particular, decision problems associated with hydropower operations motivate the application of stochastic programming. When complex decision-support problems are considered, the corresponding stochastic programming models often grow too large to store and solve on a single computer. This warrants a need for parallel approaches to enable efficient treatment of large-scale stochastic programs in a distributed environment. In this thesis, we develop mathematical and computational tools to efficiently store and solve distributed stochastic programs.

      First, we present a software framework for stochastic programming implemented in the Julia programming language. A key feature of the framework is the support for distributing stochastic programs in memory. Moreover, the framework includes a large set of structure-exploiting algorithms for solving stochastic programming problems. These algorithms are based on the classical L-shaped, progressive-hedging, and quasi-gradient algorithms and can be run in parallel on distributed stochastic programs. The distributed performance of our software framework is improved by exploring algorithmic innovations and software patterns. We present the architecture of the framework and highlight key implementation details. Finally, we provide illustrative examples of stochastic programming functionality and benchmarks on large-scale problems.

      Then, we pursue further algorithmic improvements to the distributed L-shaped algorithm. Specifically, we consider the use of dynamic cut aggregation. We develop theoretical results on convergence and complexity and then showcase performance improvements in numerical experiments. We suggest several aggregation schemes that are based on parameterized selection rules. In brief, cut aggregation can bring major performance improvements to L-shaped algorithms in distributed settings.

      Next, we consider a fast smoothing scheme for large-scale stochastic programming. We derive a smooth approximation of the subproblems in the quasi-gradient algorithm. This allows us to utilize modern acceleration methods for gradient descent. We derive problem-dependent approximation bounds and convergence properties and note a trade-off between accuracy and speed. We then pose a hybrid procedure that is both fast and accurate and show that it is competitive with the L-shaped method on large-scale benchmarks.

      Finally, we consider applications to hydropower operations. We consider three case studies in the Swedish river Skellefteälven. The day-ahead planning problem involves specifying optimal order volumes in a deregulated electricity market, without knowledge of the next-day market price, and then optimizing the hydropower production. We provide a detailed introduction to the day-ahead model and explain how it can be implemented in our framework. Using a sample-based algorithm that internally relies on our structure-exploiting solvers, we obtain tight confidence intervals around the optimal solution of the day-ahead problem. We then consider a maintenance scheduling problem as a variation of the day-ahead problem. Last, we consider a capacity expansion problem with a long planning horizon.

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    kth-thesis-Biel-2021
  • 134.
    Biel, Martin
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Distributed Stochastic Programming with Applications to Large-Scale Hydropower Operations2019Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Stochastic programming is a subfield of mathematical programming concerned with optimization problems subjected to uncertainty. Many engineering problems with random elements can be accurately modeled as a stochastic program. In particular, decision problems associated with hydropower operations motivate the application of stochastic programming. When complex decision-support problems are considered, the corresponding stochastic programming models often grow too large to store and solve on a single computer. This clarifies the need for parallel approaches that could enable efficient treatment of large-scale stochastic programs in a distributed environment. In this thesis, we develop mathematical and computational tools in order to facilitate distributed stochastic programs that can be efficiently stored and solved.

    First, we present a software framework for stochastic programming implemented in the Julia language. A key feature of the framework is the support for distributing stochastic programs in memory. Moreover, the framework includes a large set of structure-exploiting algorithms for solving stochastic programming problems. These algorithms are based on the classical L-shaped and progressive-hedging algorithms and can run in parallel on distributed stochastic programs. The distributed performance of our software tools is improved by exploring algorithmic innovations and software patterns. We present the architecture of the framework and highlight key implementation details. Finally, we provide illustrative examples of stochastic programming functionality and benchmarks on large-scale problems.

    Then, we pursue further algorithmic improvements to the distributed L-shaped algorithm. Specifically, we consider the use of dynamic cut aggregation. We develop theoretical results on convergence and complexity and then showcase performance improvements in numerical experiments. We suggest several aggregation schemes that are based on parameterized selection rules. Before we perform large-scale experiments, the aggregation parameters are determined by a tuning procedure. In brief, cut aggregation can yield major performance improvements to L-shaped algorithms in distributed settings.

    Finally, we consider an application to hydropower operations. The day-ahead planning problem involves specifying optimal order volumes in a deregulated electricity market, without knowledge of the next-day market price, and then optimizing the hydropower production. We provide a detailed introduction to the day-ahead model and explain how we can implement it with our computational tools. This covers a complete procedure of gathering data, generating forecasts from the data, and finally formulating and solving a stochastic programming model of the day-ahead problem. Using a sample-based algorithm that internally relies on our structure-exploiting solvers, we obtain tight confidence intervals around the optimal solution of the day-ahead problem.

    Download full text (pdf)
    kth-lic-biel-2019
  • 135.
    Biel, Martin
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Optimal Day-Ahead Orders Using Stochastic Programming and Noise-Driven Recurrent Neural Networks2021In: 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2021Conference paper (Refereed)
    Abstract [en]

    This paper presents a methodology for strategic day-ahead planning that uses a combination of deep learning and optimization. A noise-driven recurrent neural network structure is proposed for forecasting electricity prices and local inflow to water reservoirs. The resulting forecasters generate predictions with seasonal variation without relying on long input sequences. This forecasting method is employed in a stochastic program formulation of the day-ahead problem. This results in optimal order strategies for a price-taking hydropower producer participating in the Nordic day-ahead market. Using an open-source software framework for stochastic programming, the model is implemented and distributed over multiple cores. The model is then solved in parallel using a sampling-based algorithm. Tight confidence intervals around the stochastic solution are provided, which show that the gain from adopting a stochastic approach is statistically significant. 

  • 136.
    Biel, Martin
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Aytekin, Arda
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Johansson, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    POLO.Jl: Policy-based optimization algorithms in Julia2019In: Advances in Engineering Software, ISSN 0965-9978, E-ISSN 1873-5339, Vol. 136, article id 102695Article in journal (Refereed)
    Abstract [en]

    We present POLO. j1- a Julia package that helps algorithm developers and machine-learning practitioners design and use state-of-the-art parallel optimization algorithms in a flexible and efficient way. POLO. j1 extends our C+ + library POLO, which has been designed and implemented with the same intentions. POLO. j1 not only wraps selected algorithms in POLO and provides an easy mechanism to use data manipulation facilities and loss function definitions in Julia together with the underlying compiled C+ + library, but it also uses the policy-based design technique in a Julian way to help users prototype optimization algorithms from their own building blocks. In our experiments, we observe that there is little overhead when using the compiled C+ + code directly within Julia. We also notice that the performance of algorithms implemented in pure Julia is comparable with that of their C+ + counterparts. Both libraries are hosted on GitHub(1)under the free MIT license, and can be used easily by pulling the pre-built 64-bit architecture Docker images.(2)

  • 137.
    Biel, Martin
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Johansson, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Distributed L-shaped Algorithms in Julia2018In: PROCEEDINGS OF PAW-ATM18: 2018 IEEE/ACM PARALLEL APPLICATIONS WORKSHOP, ALTERNATIVES TO MPI (PAW-ATM) / [ed] NDERS JF, 2005, NUMER MATH, V2, P3 okhmal P., 2005, APPLICATIONS OF STOCHASTIC PROGRAMMING, V5, P609 nderoth J, 2003, COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, V24, P207 well Warren B., 2005, APPLICATIONS OF STOCHASTIC PROGRAMMING, V5, P185, IEEE , 2018, p. 57-69Conference paper (Refereed)
    Abstract [en]

    We present LShapedSolvers.jl, a suite of scalable stochastic programming solvers implemented in the Julia programming language. The solvers, which are based on the L-shaped algorithm, run efficiently in parallel, exploit problem structure, and operate on distributed data. The implementation introduces several flexible high-level abstractions that result in a modular design and simplify the development of algorithm variants. In addition, we demonstrate how the abstractions available in the Julia module for distributed computing are exploited to simplify the implementation of the parallel algorithms. The performance of the solvers is evaluated on large-scale problems for finding optimal orders on the Nordic day-ahead electricity market. With 16 worker cores, the fastest algorithm solves a distributed problem with 2.5 million variables and 1.5 million linear constraints about 19 times faster than Gurobi is able to solve the extended form directly.

  • 138.
    Biel, Martin
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Johansson, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Efficient Stochastic Programming in Julia2022In: INFORMS journal on computing, ISSN 1091-9856, E-ISSN 1526-5528, Vol. 34, no 4, p. 1885-1902Article in journal (Refereed)
    Abstract [en]

    We present StochasticPrograms.jl, a user-friendly and powerful open-source framework for stochastic programming written in the Julia language. The framework includes both modeling tools and structure-exploiting optimization algorithms. Stochastic programming models can be efficiently formulated using an expressive syntax, and models can be instantiated, inspected, and analyzed interactively. The framework scales seamlessly to distributed environments. Small instances of a model can be run locally to ensure correctness, whereas larger instances are automatically distributed in a memory-efficient way onto supercomputers or clouds and solved using parallel optimization algorithms. These structure-exploiting solvers are based on variations of the classical L-shaped, progressive-hedging, and quasi-gradient algorithms. We provide a concise mathematical background for the various tools and constructs available in the framework along with code listings exemplifying their usage. Both software innovations related to the implementation of the framework and algorithmic innovations related to the structured solvers are highlighted. We conclude by demonstrating strong scaling properties of the distributed algorithms on numerical benchmarks in a multinode setup. 

  • 139.
    Biel, Martin
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Mai, Vien V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Johansson, Mikael
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    A Fast Smoothing Procedure for Large-Scale Stochastic Programming2021In: 2021 60th IEEE conference on decision and control (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 2394-2399Conference paper (Refereed)
    Abstract [en]

    We develop a fast smoothing procedure for solving linear two-stage stochastic programs, which outperforms the well-known L-shaped algorithm on large-scale benchmarks. We derive problem-dependent bounds for the effect of smoothing and characterize the convergence rate of the proposed algorithm. The theory suggests that the smoothing scheme can be sped up by sacrificing accuracy in the final solution. To obtain an efficient and effective method, we suggest a hybrid solution that combines the speed of the smoothing scheme with the accuracy of the L-shaped algorithm. We benchmark a parallel implementation of the smoothing scheme against an efficient parallelized L-shaped algorithm on three large-scale stochastic programs, in a distributed environment with 32 worker cores. The smoothing scheme reduces the solution time by up to an order of magnitude compared to L-shaped.

  • 140.
    Biel, Martin
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Norrlof, Mikael
    Efficient Trajectory Reshaping in a Dynamic Environment2018In: 2018 IEEE 15TH INTERNATIONAL WORKSHOP ON ADVANCED MOTION CONTROL (AMC), IEEE, 2018, p. 54-59Conference paper (Refereed)
    Abstract [en]

    A general trajectory planner for optimal control problems is presented and applied to a robot system. The approach is based on timed elastic bands and nonlinear model predictive control. By exploiting the sparsity in the underlying optimization problems the computational effort can be significantly reduced, resulting in a real-time capable planner. In addition, a localization based switching strategy is employed to enforce convergence and stability. The planning procedure is illustrated in a robotics application using a realistic SCARA type robot.

  • 141.
    Bin, Elisa
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Andruetto, Claudia
    KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.
    Susilo, Yusak
    Univ Nat Resources & Life Sci BOKU, Vienna, Austria..
    Pernestål Brenden, Anna
    KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.
    The trade-off behaviours between virtual and physical activities during the first wave of the COVID-19 pandemic period2021In: European Transport Research Review, ISSN 1867-0717, E-ISSN 1866-8887, Vol. 13, no 1, article id 14Article in journal (Refereed)
    Abstract [en]

    IntroductionThe first wave of COVID-19 pandemic period has drastically changed people's lives all over the world. To cope with the disruption, digital solutions have become more popular. However, the ability to adopt digitalised alternatives is different across socio-economic and socio-demographic groups.ObjectiveThis study investigates how individuals have changed their activity-travel patterns and internet usage during the first wave of the COVID-19 pandemicperiod, and which of these changes may be kept.MethodsAn empirical data collection was deployed through online forms. 781 responses from different countries (Italy, Sweden, India and others) have beencollected, and a series of multivariate analyses was carried out. Two linear regression models are presented, related to the change of travel activities andinternet usage, before and during the pandemic period. Furthermore, a binary regression model is used to examine the likelihood of the respondents to adoptand keep their behaviours beyond the pandemic period.ResultsThe results show that the possibility to change the behaviour matter. External restrictions and personal characteristics are the driving factors of the reductionin ones' daily trips. However, the estimation results do not show a strong correlation between the countries' restriction policy and the respondents' likelihoodto adopt the new and online-based behaviours for any of the activities after the restriction period.ConclusionThe acceptance and long-term adoption of the online alternatives for activities are correlated with the respondents' personality and socio-demographicgroup, highlighting the importance of promoting alternatives as a part of longer-term behavioural and lifestyle changes.

  • 142.
    Bisoffi, Andrea
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Beerens, R.
    Eindhoven Univ Technol, Dept Mech Engn, POB 513, NL-5600 MB Eindhoven, Netherlands..
    Zaccarian, L.
    Univ Toulouse, CNRS, LAAS, F-31400 Toulouse, France.;Univ Trento, Dept Ind Engn, I-38122 Trento, Italy..
    Heemels, W. P. M. H.
    Eindhoven Univ Technol, Dept Mech Engn, POB 513, NL-5600 MB Eindhoven, Netherlands..
    Nijmeijer, H.
    Eindhoven Univ Technol, Dept Mech Engn, POB 513, NL-5600 MB Eindhoven, Netherlands..
    van de Wouw, N.
    Eindhoven Univ Technol, Dept Mech Engn, POB 513, NL-5600 MB Eindhoven, Netherlands.;Univ Minnesota, Dept Civil Environm & GeoEngn, Minneapolis, MN 55455 USA..
    Hybrid model formulation and stability analysis of a PID-controlled motion system with Coulomb friction2019In: IFAC PAPERSONLINE, ELSEVIER , 2019, Vol. 52, no 16, p. 84-89Conference paper (Refereed)
    Abstract [en]

    For a PID-controlled motion system under Coulomb friction described by a differential inclusion, we present a hybrid model comprising logical states indicating whether the closed loop is in stick or in slip, thereby resembling a hybrid automaton. A key step for this description is the addition of a timer exploiting a peculiar semiglobal dwell time of the original dynamics, which then removes defective and unwanted nonconverging Zeno solutions from the hybrid model. Through it, we then revisit an existing proof of global asymptotic stability, which is significantly simplified by way of a smooth weak Lyapunov function. The relevance of the proposed hybrid representation is also illustrated on a novel control strategy resetting the PID integrator and hinging upon the proposed hybrid model.

  • 143.
    Bisoffi, Andrea
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    A hybrid barrier certificate approach to satisfy linear temporal logic specifications2018In: 2018 Annual American Control Conference (ACC), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 634-639, article id 8430795Conference paper (Refereed)
    Abstract [en]

    In this work we formulate the satisfaction of a (syntactically co-safe) linear temporal logic specification on a physical plant through a recent hybrid dynamical systems formalism. In order to solve this problem, we introduce an extension to such a hybrid system framework of the so-called eventuality property, which matches suitably the condition for the satisfaction of such a temporal logic specification. The eventuality property can be established through barrier certificates, which we derive for the considered hybrid system framework. Using a hybrid barrier certificate, we propose a solution to the original problem. Simulations illustrate the effectiveness of the proposed method. 2018 AACC.

  • 144.
    Bisoffi, Andrea
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Satisfaction of Linear Temporal Logic Specifications Through Recurrence Tools for Hybrid Systems2021In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 66, no 2, p. 818-825Article in journal (Refereed)
    Abstract [en]

    In this article, we formulate the problem of satisfying a linear temporal logic formula on a linear plant with output feedback, through a recent hybrid systems formalism. We relate this problem to the notion of recurrence introduced for the considered formalism, and we then extend Lyapunov-like conditions for recurrence of an open, unbounded set. One of the proposed relaxed conditions allows certifying recurrence of a suitable set, and this guarantees that the high-level evolution of the plant satisfies the formula, without relying on discretizations of the plant. Simulations illustrate the proposed approach.

  • 145.
    Bisoffi, Andrea
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Marcazzan, A.
    Oboe, R.
    Reset solutions for performance limitations induced by coulomb friction in a motion control system with a disturbance observer2019In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, Institute of Electrical and Electronics Engineers (IEEE) , 2019, p. 1187-1192Conference paper (Refereed)
    Abstract [en]

    In a proportional-integral-derivative (PID) motion control system under Coulomb friction, a suitable reset of the integral action was recently shown in [1] to reduce significantly the settling time. Motivated by the benefits of disturbance-observer schemes (DOB) over PID schemes, we show that the DOB scheme is prone to an analogous performance limitation in the presence of Coulomb friction and extend then the reset strategy in [1] for the DOB scheme. The working principle and the effectiveness of the solution is illustrated in simulations.

  • 146. Bjork, J.
    et al.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Dorfler, F.
    Dynamic Virtual Power Plant Design for Fast Frequency Reserves: Coordinating Hydro and Wind2022In: IEEE Transactions on Control of Network Systems, E-ISSN 2325-5870, p. 1-12Article in journal (Refereed)
    Abstract [en]

    To ensure frequency stability in future low-inertia power grids, fast ancillary services such as fast frequency reserves (FFR) have been proposed. In this work, the coordination of conventional (slow) frequency containment reserves (FCR) with FFR is treated as a decentralized model matching problem. The design results in a dynamic virtual power plant (DVPP) whose aggregated output fulfills the system operator’s (SO’s) requirements in all time scales, while accounting for the capacity and bandwidth limitation of participating devices. This is illustrated in a 5-machine representation of the Nordic synchronous grid. In the Nordic grid, stability issues and bandwidth limitations associated with non-minimum phase zeros of hydropower is a well-known problem. By simulating the disconnection of a 1400 MW importing dc link, it is shown that the proposed DVPP design allows for coordinating fast FFR from wind, with slow FCR from hydro, while respecting dynamic limitations of all participating devices. The SO’s requirements are fulfilled in a realistic low-inertia scenario without the need to install battery storage or to waste wind energy by curtailing the wind turbines. 

  • 147.
    Björk, Joakim
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Kungliga Tekniska högskolan.
    Fundamental Control Performance Limitations for Interarea Oscillation Damping and Frequency Stability2021Doctoral thesis, monograph (Other academic)
    Abstract [en]

    With the transition towards renewable energy and the deregulation of the electricity markets, the power system is changing. Growing electricity demand and more intermittent power production increase the need for transfer capacity. Lower inertia levels due to a higher share of renewables increase the need for fast frequency reserves (FFR). In this thesis, we study fundamental control limitations for improving the damping of interarea oscillations and frequency stability.

    The first part of the thesis considers the damping of oscillatory interarea modes. These system-wide modes involve power oscillating between groups of generators and are sometimes hard to control due to their scale and complexity. We consider limitations of decentralized control schemes based on local measurements, as well as centralized control schemes with limitations associated to actuator dynamics and network topology. It is shown that the stability of asynchronous grids can be improved by modulating the active power of a single interconnecting high-voltage direct current (HVDC) link. One challenge with modulating HVDC active power is that the interaction between interarea modes of the two grids may have a negative impact on system stability. By studying the controllability Gramian, we show that it is possible to improve the damping in both grids as long as the frequencies of their interarea modes are not too close. It is demonstrated how the controllability, and therefore the achievable damping, deteriorates as the frequency difference becomes small. With a modal frequency difference of 5%, the damping can be improved by around 2 percentage points whereas a modal frequency difference of 20% allows for around 8 percentage points damping improvement. The results are validated by simulating two HVDC-interconnected 32-bus power system models. We also consider the coordinated control of two and more HVDC links. For some network configurations, it is shown that the interaction between troublesome interarea modes can be avoided. 

    The second part considers the coordination of frequency containment reserves (FCR) in low-inertia power systems. A case study is performed in a 5-machine model of the Nordic synchronous grid. We consider a low-inertia test case where FCR are provided by hydro power. The non-minimum phase characteristic of the waterways limits the achievable bandwidth of the FCR control. It is shown that a consequence of this is that hydro-FCR fails at keeping the frequency nadir above the 49.0 Hz safety limit following the loss of a HVDC link that imports 1400 MW. To improve the dynamic frequency stability, FFR from wind power is considered. For this, a new wind turbine model is developed. The turbine is controlled at variable-speed, enabling FFR by temporarily borrowing energy from the rotating turbine. The nonlinear wind turbine dynamics are linearized to facilitate a control design that coordinate FFR from the wind with slow FCR from hydropower. Complementary wind resources with a total rating of 2000 MW, operating at 70–90% rated wind speeds, is shown to be more than enough to fulfill the frequency stability requirements. The nadir is kept above 49.0 Hz without the need to install battery storage or to waste wind energy by curtailing the wind turbines.

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  • 148.
    Björk, Joakim
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Performance Quantification of Interarea Oscillation Damping Using HVDC2019Licentiate thesis, monograph (Other academic)
    Abstract [en]

    With the transition towards renewable energy, and the deregulation of the electricity market, generation patterns and grid topology are changing. These changes increase the need for transfer capacity. One limiting factor, which sometimes leads to underutilization of the transmission grid, is interarea oscillations. These system-wide modes involve groups of generators oscillating relative to each other and are sometimes hard to control due to their scale and complexity. In this thesis we investigate how high-voltage direct current (HVDC) transmission can be used to attenuate interarea oscillations. The thesis has two main contributions.

    In the first contribution we show how the stability of two asynchronous grids can be improved by modulating the active power of a single interconnecting HVDC link. One concern with modulating HVDC active power is that the interaction between interarea modes of the two grids may have a negative impact on system stability. By studying the controllability Gramian, we show that it is always possible to improve the damping in both grids as long as the frequencies of their interarea modes are not too close. For simplified models, it is explicitly shown how the controllability, and therefore the achievable damping improvements, deteriorates as the frequency difference becomes small.

    The second contribution of the thesis is to show how coordinated control of two (or more) links can be used to avoid interaction between troublesome interarea modes. We investigate the performance of some multivariable control designs. In particular we look at input usage as well as robustness to measurement, communication, and actuator failures. Suitable controllers are thereby characterized.

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    fulltext
  • 149.
    Björk, Joakim
    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).
    Control Limitations due to Zero Dynamics in a Single-Machine Infinite Bus Network2020In: Ifac papersonline, Elsevier BV , 2020, Vol. 53, no 2, p. 13531-13538Conference paper (Refereed)
    Abstract [en]

    In this work, fundamental control limitations for rotor angle stability are considered. Limitations are identified by characterizing open-loop transfer function zeros for input-output combinations of certain power system configurations. Of particular interest are non-minimum phase (NMP) zeros that limit the achievable performance of the closed-loop system. By studying a single-machine infinite bus power system model, analytic conditions for the presence of NMP zeros are derived. They are shown to be closely linked to the destabilizing effect of automatic voltage regulators (AVRs). Depending on the control loop, it is found that NMP zeros may persist in the system even if the closed-loop system is stabilized through feedback control. A simulation study shows that NMP zeros introduced by AVR limit the achievable performance and stabilization using feedback control. 

  • 150.
    Björk, Joakim
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Harnefors, Lennart
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Fundamental Performance Limitations in Utilizing HVDC to Damp Interarea Modes2019In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 34, no 2, p. 1095-1104Article in journal (Refereed)
    Abstract [en]

    This paper considers power oscillation damping (POD) using active power modulation of high-voltage dc transmissions. An analytical study of how the proximity between interarea modal frequencies in two interconnected asynchronous grids puts a fundamental limit to the achievable performance is presented. It is shown that the ratio between the modal frequencies is the sole factor determining the achievable nominal performance. To illustrate the inherent limitations, simulations using a proportional controller tuned to optimize performance in terms of POD are done on a simplified two-machine model. The influence of limited system information and unmodeled dynamics is shown. The analytical result is then further validated on a realistic model with two interconnected 32-bus networks.

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