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  • 1.
    Abdalmoaty, Mohamed
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors2017Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. Albeit asymptotically optimal, these methods come with several computational challenges and fundamental limitations.

    The contributions of this thesis can be divided into two main parts. In the first part, approximate solutions to the maximum likelihood problem are explored. Both analytical and numerical approaches, based on the expectation-maximization algorithm and the quasi-Newton algorithm, are considered. While analytic approximations are difficult to analyze, asymptotic guarantees can be established for methods based on Monte Carlo approximations. Yet, Monte Carlo methods come with their own computational difficulties; sampling in high-dimensional spaces requires an efficient proposal distribution to reduce the number of required samples to a reasonable value.

    In the second part, relatively simple prediction error method estimators are proposed. They are based on non-stationary one-step ahead predictors which are linear in the observed outputs, but are nonlinear in the (assumed known) input. These predictors rely only on the first two moments of the model and the computation of the likelihood function is not required. Consequently, the resulting estimators are defined via analytically tractable objective functions in several relevant cases. It is shown that, under mild assumptions, the estimators are consistent and asymptotically normal. In cases where the first two moments are analytically intractable due to the complexity of the model, it is possible to resort to vanilla Monte Carlo approximations. Several numerical examples demonstrate a good performance of the suggested estimators in several cases that are usually considered challenging.

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  • 2.
    Abdalmoaty, Mohamed R.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem⁎2018In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 784-789Article in journal (Refereed)
    Abstract [en]

    The estimation problem of stochastic Wiener-Hammerstein models is recognized to be challenging, mainly due to the analytical intractability of the likelihood function. In this contribution, we apply a computationally attractive prediction error method estimator to a real-data stochastic Wiener-Hammerstein benchmark problem. The estimator is defined using a deterministic predictor that is nonlinear in the input. The prediction error method results in tractable expressions, and Monte Carlo approximations are not necessary. This allows us to tackle several issues considered challenging from the perspective of the current mainstream approach. Under mild conditions, the estimator can be shown to be consistent and asymptotically normal. The results of the method applied to the benchmark data are presented and discussed.

  • 3.
    Abdalmoaty, Mohamed R.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Rojas, Cristian R.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Identification of a Class of Nonlinear Dynamical Networks⁎2018In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 868-873Article in journal (Refereed)
    Abstract [en]

    Identification of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; consider, for example, two stochastic linear time-invariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal one-step ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closed-form analytical expressions in several non-trivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some block-oriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.

  • 4.
    Abdalmoaty, Mohamed
    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.
    Bereza-Jarocinski, Robert
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    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).
    Identification of Non-Linear Differential-Algebraic Equation Models with Process Disturbances2021In: Proceedings The 60th IEEE conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2021Conference paper (Refereed)
    Abstract [en]

    Differential-algebraic equations (DAEs) arise naturally as a result of equation-based object-oriented modeling. In many cases, these models contain unknown parameters that have to be estimated using experimental data. However, often the system is subject to unknown disturbances which, if not taken into account in the estimation, can severely affect the model's accuracy. For non-linear state-space models, particle filter methods have been developed to tackle this issue. Unfortunately, applying such methods to non-linear DAEs requires a transformation into a state-space form, which is particularly difficult to obtain for models with process disturbances. In this paper, we propose a simulation-based prediction error method that can be used for non-linear DAEs where disturbances are modeled as continuous-time stochastic processes. To the authors' best knowledge, there are no general methods successfully dealing with parameter estimation for this type of model. One of the challenges in particle filtering  methods are random variations in the minimized cost function due to the nature of the algorithm. In our approach, a similar phenomenon occurs and we explicitly consider how to sample the underlying continuous process to mitigate this problem. The method is illustrated numerically on a pendulum example. The results suggest that the method is able to deliver consistent estimates.

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  • 5.
    Abdalmoaty, Mohamed
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Henrion, D.
    Rodrigues, L.
    Measures and LMIs for optimal control of piecewise-affine systems2013In: 2013 European Control Conference, ECC 2013, IEEE, 2013, p. 3173-3178, article id 6669627Conference paper (Refereed)
    Abstract [en]

    This paper considers the class of deterministic continuous-time optimal control problems (OCPs) with piecewise-affine (PWA) vector field, polynomial Lagrangian and semialgebraic input and state constraints. The OCP is first relaxed as an infinite-dimensional linear program (LP) over a space of occupation measures. This LP is then approached by an asymptotically converging hierarchy of linear matrix inequality (LMI) relaxations. The relaxed dual of the original LP returns a polynomial approximation of the value function that solves the Hamilton-Jacobi-Bellman (HJB) equation of the OCP. Based on this polynomial approximation, a suboptimal policy is developed to construct a state feedback in a sample-and-hold manner. The results show that the suboptimal policy succeeds in providing a suboptimal state feedback law that drives the system relatively close to the optimal trajectories and respects the given constraints.

  • 6.
    Abdalmoaty, Mohamed
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem2018In: 18th IFAC Symposium on System Identification, 2018Conference paper (Refereed)
    Abstract [en]

    The estimation problem of stochastic Wiener-Hammerstein models is recognized to be challenging, mainly due to the analytical intractability of the likelihood function. In this contribution, we apply a computationally attractive prediction error method estimator to a real-data stochastic Wiener-Hammerstein benchmark problem. The estimator is defined using a deterministic predictor that is nonlinear in the input. The prediction error method results in tractable expressions, and Monte Carlo approximations are not necessary. This allows us to tackle several issues considered challenging from the perspective of the current mainstream approach. Under mild conditions, the estimator can be shown to be consistent and asymptotically normal. The results of the method applied to the benchmark data are presentedand discussed.

    Download full text (pdf)
    0028.pdf
  • 7.
    Abdalmoaty, Mohamed
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Consistent Estimators of Stochastic MIMO Wiener Models based on Suboptimal Predictors2018Conference paper (Refereed)
    Download full text (pdf)
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  • 8.
    Abdalmoaty, Mohamed
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Linear Prediction Error Methods for Stochastic Nonlinear Models2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 105, p. 49-63Article in journal (Refereed)
    Abstract [en]

    The estimation problem for stochastic parametric nonlinear dynamical models is recognized to be challenging. The main difficulty is the intractability of the likelihood function and the optimal one-step ahead predictor. In this paper, we present relatively simple prediction error methods based on non-stationary predictors that are linear in the outputs. They can be seen as extensions of the linear identification methods for the case where the hypothesized model is stochastic and nonlinear. The resulting estimators are defined by analytically tractable objective functions in several common cases. It is shown that, under certain identifiability and standard regularity conditions, the estimators are consistent and asymptotically normal. We discuss the relationship between the suggested estimators and those based on second-order equivalent models as well as the maximum likelihood method. The paper is concluded with a numerical simulation example as well as a real-data benchmark problem.

    Download full text (pdf)
    fulltext
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    fulltext
  • 9.
    Abdalmoaty, Mohamed
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Simulated Pseudo Maximum Likelihood Identification of Nonlinear Models2017In: The 20th IFAC World Congress, Elsevier, 2017, Vol. 50, p. 14058-14063Conference paper (Refereed)
    Abstract [en]

    Nonlinear stochastic parametric models are widely used in various fields. However, for these models, the problem of maximum likelihood identification is very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the analytically intractable likelihood function and compute either the maximum likelihood or a Bayesian estimator. These methods, albeit asymptotically optimal, are computationally expensive. In this contribution, we present a simulation-based pseudo likelihood estimator for nonlinear stochastic models. It relies only on the first two moments of the model, which are easy to approximate using Monte-Carlo simulations on the model. The resulting estimator is consistent and asymptotically normal. We show that the pseudo maximum likelihood estimator, based on a multivariate normal family, solves a prediction error minimization problem using a parameterized norm and an implicit linear predictor. In the light of this interpretation, we compare with the predictor defined by an ensemble Kalman filter. Although not identical, simulations indicate a close relationship. The performance of the simulated pseudo maximum likelihood method is illustrated in three examples. They include a challenging state-space model of dimension 100 with one output and 2 unknown parameters, as well as an application-motivated model with 5 states, 2 outputs and 5 unknown parameters.

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  • 10.
    Abdalmoaty, Mohamed
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Hjalmarsson, Håkan
    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).
    The Gaussian MLE versus the Optimally weighted LSE2020In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 37, no 6, p. 195-199Article in journal (Refereed)
    Abstract [en]

    In this note, we derive and compare the asymptotic covariance matrices of two parametric estimators: the Gaussian Maximum Likelihood Estimator (MLE), and the optimally weighted Least-Squares Estimator (LSE). We assume a general model parameterization where the model's mean and variance are jointly parameterized, and consider Gaussian and non-Gaussian data distributions.

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    fulltext
  • 11.
    Abdalmoaty, Mohamed
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Identication of a Class of Nonlinear Dynamical Networks2018Conference paper (Refereed)
    Abstract [en]

    Identifcation of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; consider, for example, two stochastic linear time-invariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal one-step ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closed-form analytical expressions in several non-trivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some block-oriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.

    Download full text (pdf)
    0131.pdf
  • 12.
    AbdElKhalek, Y. M.
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Awad, M. I.
    Abd El Munim, H. E.
    Maged, S. A.
    Trajectory-based fast ball detection and tracking for an autonomous industrial robot system2021In: International Journal of Intelligent Systems Technologies and Applications, ISSN 1740-8865, E-ISSN 1740-8873, Vol. 20, no 2, p. 126-145Article in journal (Refereed)
    Abstract [en]

    Autonomising industrial robots is the main goal in this paper; imagine humanoid robots that have several degrees of freedom (DOF) mechanisms as their arms. What if the humanoid's arms could be programmed to be responsive to their surrounding environment, without any hard-coding assigned? This paper presents the idea of an autonomous system, where the system observes the surrounding environment and takes action on its observation. The application here is that of rebuffing an object that is thrown towards a robotic arm's workspace. This application mimics the idea of high dynamic responsiveness of a robot's arm. This paper will present a trajectory generation framework for rebuffing incoming flying objects. The framework bases its assumptions on inputs acquired through image processing and object detection. After extensive testing, it can be said that the proposed framework managed to fulfil the real-time system requirements for this application, with an 80% successful rebuffing rate. 

  • 13.
    Abeynanda, Hansi
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Weeraddana, Chathuranga
    Univ Oulu, Ctr Wireless Commun, Oulu 90014, Finland..
    Lanel, G. H. J.
    Univ Sri Jayewardenepura, Dept Math, Nugegoda 10250, Sri Lanka..
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    On the Primal Feasibility in Dual Decomposition Methods Under Additive and Bounded Errors2023In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 71, p. 655-669Article in journal (Refereed)
    Abstract [en]

    With the unprecedented growth of signal processing and machine learning application domains, there has been a tremendous expansion of interest in distributed optimization methods to cope with the underlying large-scale problems. Nonetheless, inevitable system-specific challenges such as limited computational power, limited communication, latency requirements, measurement errors, and noises in wireless channels impose restrictions on the exactness of the underlying algorithms. Such restrictions have appealed to the exploration of algorithms' convergence behaviors under inexact settings. Despite the extensive research conducted in the area, it seems that the analysis of convergences of dual decomposition methods concerning primal optimality violations, together with dual optimality violations is less investigated. Here, we provide a systematic exposition of the convergence of feasible points in dual decomposition methods under inexact settings, for an important class of global consensus optimization problems. Convergences and the rate of convergences of the algorithms are mathematically substantiated, not only from a dual-domain standpoint but also from a primal-domain standpoint. Analytical results show that the algorithms converge to a neighborhood of optimality, the size of which depends on the level of underlying distortions.

  • 14.
    Abolpour, Roozbeh
    et al.
    Shiraz Univ, Sch Elect & Comp Engn, Shiraz, Iran..
    Dehghani, Maryam
    Shiraz Univ, Sch Elect & Comp Engn, Shiraz, Iran..
    Hesamzadeh, Mohammad Reza
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Inside-Ellipsoid Outside-Sphere (IEOS) model for general bilinear feasibility problems: Feasibility analysis and solution algorithm2023In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 147, p. 110738-, article id 110738Article in journal (Refereed)
    Abstract [en]

    This paper deals with general bilinear feasibility problems. A nonlinear transformation is introduced that reformulates a general bilinear feasibility problem as a Linear Matrix Inequality (LMI) problem augmented with a single non-convex quadratic constraint. The single non-convex quadratic constraint has a regular concave constraint function. Due to the LMI part of this formulation, it is easier to analyze, and we prove that the solution space of this formulation is located inside several ellipsoids and outside a sphere. This leads to our proposed Inside-Ellipsoid and Outside-Sphere (IEOS) model for general bilinear feasibility problems. Then, the feasibility analysis of our proposed IEOS model is performed. The related necessary feasibility conditions and sufficient feasibility conditions are theoretically developed. Moreover, an iterative algorithm for solving our IEOS model is also proposed.Two applications including matrix-factorization problem in control systems and power-flow prob-lem in power systems are considered to evaluate the practicality of our proposed approach. Both problems are formulated as IEOS models. It is shown that our proposed model can provide more accurate solutions to these problems as compared to previous competing approaches in the relevant literature.

  • 15.
    Abolpour, Roozbeh
    et al.
    Shiraz Univ, Sch Elect & Comp Engn, Shiraz 7134851154, Iran..
    Hesamzadeh, Mohammad Reza
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Dehghani, Maryam
    Shiraz Univ, Sch Elect & Comp Engn, Shiraz 7134851154, Iran..
    A New Power Flow Model With a Single Nonconvex Quadratic Constraint: The LMI Approach2022In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 37, no 2, p. 1218-1229Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a new mathematical model for power flow problem based on the linear and nonlinear matrix inequality theory. We start with rectangular model of power flow (PF) problem and then reformulate it as a Bilinear Matrix Inequality (BMI) model. A Theorem is proved which is able to convert this BMI model to a Linear Matrix Inequality (LMI) model along with One Nonconvex Quadratic Constraint (ONQC). Our proposed LMI-ONQC model for PF problem has only one single nonconvex quadratic constraint irrespective of the network size, while in the rectangular and BMI models the number of nonconvex constraints grows as the network size grows. This interesting property leads to reduced complexity level in our LMI-ONQC model which in turn makes it easier to solve for finding a PF solution. The non-conservativeness, iterative LMI solvability, well-defined and easy-to-understand geometry and pathwise connectivity of feasibility region are other important properties of proposed LMI-ONQC model which are discussed in this paper. An illustrative two-bus example is carefully studied to show different properties of our LMI-ONQC model. We have also tested our LMI-ONQC model on 30 different power-system cases including four ill-conditioned systems and compared it with a group of existing approaches. The numerical results show the promising performance of our LMI-ONQC model and its solution algorithm to find a PF solution.

  • 16.
    Abolpour, Roozbeh
    et al.
    Shiraz Univ, Sch Elect & Comp Engn, Shiraz, Iran..
    Javanmardi, Hamidreza
    Shiraz Univ, Sch Elect & Comp Engn, Shiraz, Iran..
    Dehghani, Maryam
    Shiraz Univ, Sch Elect & Comp Engn, Shiraz, Iran..
    Hesamzadeh, Mohammad Reza
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Optimal frequency regulation in an uncertain islanded microgrid: A modified direct search algorithm2022In: IET Renewable Power Generation, ISSN 1752-1416, E-ISSN 1752-1424, Vol. 16, no 4, p. 726-739Article in journal (Refereed)
    Abstract [en]

    This paper presents a methodology for frequency regulation in a microgrid involving renewable energy sources (RES) using a dynamic controller, which is an output feedback controller (OFC). The parameters of OFC are tuned by searching the design space of the controller. Since the RES model is not exactly known, the uncertain model is derived and the OFC is considered for it. The goal of controller tuning is to find appropriate parameters of the controller such that the norm of frequency deviations, even in presence of uncertainties in the RES parameters is minimized. An algorithm based on searching the controller design space is suggested to find the suitable controller gains. The algorithm assumes the controller parameters lie in a convex space and searches the space systematically such that an appropriate solution is found. The method is proved mathematically and two theorems are mentioned, accordingly. Finally, a simulated model of a RES is utilized for algorithm evaluation and the results demonstrate the algorithm capability in optimal frequency regulation.

  • 17. Abu-Rmileh, Amjad
    et al.
    Garcia-Gabin, Winston
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Detection and Prevention of Hypoglycemia in Automated Insulin Delivery Systems for Type 1 Diabetes Patients2012In: Advances in Medicine and Biology / [ed] Leon V. Berhardt, Nova Science Publishers, Inc., 2012, p. 249-266Chapter in book (Refereed)
  • 18. Abu-Rmileh, Amjad
    et al.
    Garcia-Gabin, Winston
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Hypoglycemia prevention in closed-loop artificial pancreas for patients with type 1 diabetes2011In: Diabetes: Damages and treatments / [ed] Everlon Cid Rigobelo, IN-TECH, 2011, p. 207-226Chapter in book (Refereed)
  • 19.
    Abu-Rmileh, Amjad
    et al.
    Universidad de Gerona.
    Garcia-Gabin, Winston
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Smith Predictor Sliding Mode Closed-loop Glucose Controller in Type 1 Diabetes2011In: Proceedings of the 18th IFAC World Congress, 2011, 2011Conference paper (Refereed)
    Abstract [en]

    Type 1 diabetic patients depend on external insulin delivery to keep their blood glucose within near-normal ranges. In this work, two robust closed-loop controllers for blood glucose control are developed to prevent the life-threatening hypoglycemia, as well as to avoid extended hyperglycemia. The proposed controllers are designed by using the sliding mode control technique in a Smith predictor structure. To improve meal disturbance rejection, a simple feedforward controller is added to inject meal-time insulin bolus. Simulation studies were used to test the controllers, and shown the controllers ability to regulate the blood glucose within the safe limits in the presence of errors in measurements, modeling, and meal estimation.

  • 20. Abu-Rmileh, Amjad
    et al.
    Garcia-Gabin, Winston
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Wiener sliding-mode control for artificial pancreas: A new nonlinear approach to glucose regulation2012In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, ISSN 0169-2607, Vol. 107, no 2, p. 327-340Article in journal (Refereed)
    Abstract [en]

    Type 1 diabetic patients need insulin therapy to keep their blood glucose close to normal. In this paper an attempt is made to show how nonlinear control-oriented model may be used to improve the performance of closed-loop control of blood glucose in diabetic patients. The nonlinear Wiener model is used as a novel modeling approach to be applied to the glucose control problem. The identified Wiener model is used in the design of a robust nonlinear sliding mode control strategy. Two configurations of the nonlinear controller are tested and compared to a controller designed with a linear model. The controllers are designed in a Smith predictor structure to reduce the effect of system time delay. To improve the meal compensation features, the controllers are provided with a simple feedforward controller to inject an insulin bolus at meal time. Different simulation scenarios have been used to evaluate the proposed controllers. The obtained results show that the new approach out-performs the linear control scheme, and regulates the glucose level within safe limits in the presence of measurement and modeling errors, meal uncertainty and patient variations.

  • 21.
    Ackeberg, Anders
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Control of Periodic Solutions in Chemical Reactors2003Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
  • 22.
    Adaldo, Antonio
    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).
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Cloud-supported effective coverage of 3D structures2018In: 2018 European Control Conference, ECC 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 95-100, article id 8550377Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a distributed algorithm for cloud-supported effective coverage of 3D structures with a network of sensing agents. The structure to inspect is abstracted into a set of landmarks, where each landmark represents a point or small area of interest, and incorporates information about position and orientation. The agents navigate the environment following the proposed control algorithm until all landmarks have reached a satisfactory level of coverage. The agents do not communicate with each other directly, but exchange data through a shared cloud repository which is accessed asynchronously and intermittently. We show formally that, under the proposed control architecture, the networked agents complete the coverage mission in finite time. The results are corroborated by simulations in ROS, and experimental evaluation is in progress.

  • 23.
    Adaldo, Antonio
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hybrid coverage and inspection control for anisotropic mobile sensor teams2017In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 50, no 1, p. 613-618Article in journal (Refereed)
    Abstract [en]

    In this paper, we present an algorithm for pose control of a team of mobile sensors for coverage and inspection applications. The region to cover is abstracted into a finite set of landmarks, and each sensor is responsible to cover some of the landmarks. The sensors progressively improve their coverage by adjusting their poses and by transferring the ownership of some landmarks to each other. Inter-sensor communication is pairwise and intermittent. The sensor team is formally modeled as a multi-agent hybrid system, and an invariance argument formally shows that the team reaches an equilibrium configuration, while a global coverage measure is improving monotonically. A numerical simulation corroborates the theoretical results.

  • 24.
    Adaldo, Antonio
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Liuzza, D.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Coordination of multi-agent systems with intermittent access to a cloud repository2017In: Workshop on Sensing and Control for Autonomous Vehicles: Applications to Land, Water and Air Vehicles, 2017, Springer, 2017, Vol. 474, p. 453-471Conference paper (Refereed)
    Abstract [en]

    A cloud-supported multi-agent system is composed of autonomous agents required to achieve a common coordination objective by exchanging data over a shared cloud repository. The repository is accessed asychronously by different agents, and direct inter-agent commuication is not possible. This model is motivated by the problem of coordinating a fleet of autonomous underwater vehicles, with the aim to avoid the use of expensive and power-hungry modems for underwater communication. For the case of agents with integrator dynamics, a control law and a rule for scheduling the cloud access are formally defined and proven to achieve the desired coordination. A numerical simulation corroborate the theoretical results.

  • 25.
    Adaldo, Antonio
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Liuzza, Davide
    Univ Sannio, Dept Engn, I-82100 Benevento, Italy..
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Cloud-Supported Formation Control of Second-Order Multiagent Systems2018In: IEEE Transactions on Control of Network Systems, E-ISSN 2325-5870, Vol. 5, no 4, p. 1563-1574Article in journal (Refereed)
    Abstract [en]

    This paper addresses a formation problem for a network of autonomous agents with second-order dynamics and bounded disturbances. Coordination is achieved by having the agents asynchronously upload (download) data to (from) a shared repository, rather than directly exchanging data with other agents. Well-posedness of the closed-loop system is demonstrated by showing that there exists a lower bound for the time interval between two consecutive agent accesses to the repository. Numerical simulations corroborate the theoretical results.

  • 26.
    Adaldo, Antonio
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Liuzza, Davide
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Multi-Agent Trajectory Tracking with Self-Triggered Cloud Access2016In: 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 2207-2214, article id 7798591Conference paper (Refereed)
    Abstract [en]

    This paper presents a cloud-supported control algorithm for coordinated trajectory tracking of networked autonomous agents. The motivating application is the coordinated control of Autonomous Underwater Vehicles. The control objective is to have the vehicles track a reference trajectory while keeping an assigned formation. Rather than relying on inter-agent communication, which is interdicted underwater, coordination is achieved by letting the agents intermittently access a shared information repository hosted on a cloud. An event-based law is proposed to schedule the accesses of each agent to the cloud. We show that, with the proposed scheduling of the cloud accesses, the agents achieve the required coordination objective. Numerical simulations corroborate the theoretical results.

  • 27.
    Adaldo, Antonio
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Mansouri, S. S.
    Kanellakis, C.
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Nikolakopoulos, G.
    Cooperative coverage for surveillance of 3D structures2017In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1838-1845Conference paper (Refereed)
    Abstract [en]

    In this article, we propose a planning algorithm for coverage of complex structures with a network of robotic sensing agents, with multi-robot surveillance missions as our main motivating application. The sensors are deployed to monitor the external surface of a 3D structure. The algorithm controls the motion of each sensor so that a measure of the collective coverage attained by the network is nondecreasing, while the sensors converge to an equilibrium configuration. A modified version of the algorithm is also provided to introduce collision avoidance properties. The effectiveness of the algorithm is demonstrated in a simulation and validated experimentally by executing the planned paths on an aerial robot.

  • 28.
    Afzal, Muhammad
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Heat and Power Technology.
    Saine, Kari
    Wärtsilä Finland OyResearch and Development, WSSCVaasaFinland.
    Paro, Claus
    Wärtsilä Finland OyResearch and Development, WSSCVaasaFinland.
    Dascotte, Eddy
    Dynamic Design Solutions (DDS) NVLeuvenBelgium.
    Experimental evaluation of the inertia properties of large diesel engines2021In: Conference Proceedings of the Society for Experimental Mechanics Series, Springer , 2021, p. 205-213Conference paper (Refereed)
    Abstract [en]

    Inertia properties of several medium speed large diesel engines are evaluated using the Inertia Restrain Method (IRM). This method requires measuring frequency response functions (FRFs) at several well-chosen locations and under dynamic loading in different directions that stimulate rigid body movements. The mass line values of the measured FRFs are then evaluated and, together with the sensor locations, are used by IRM to determine center of gravity, mass and mass moments of inertia. The aim of the study is to investigate the accuracy and robustness of the IRM for extracting the inertia properties of complex structures. Therefore, several line- and V-engines were measured. The experimental results are compared with finite element models and result obtained from weighing tests. Different types of excitation source such as hammer and shaker are used to excite the structure. The result obtained from two excitation sources are compared and discussed. The effect of measurement point locations and driving point accelerometers on the FRFs and inertia properties are investigated. The extracted inertia properties in all cases are considered sufficiently accurate. This indicates that the IRM is a robust tool for identifying the inertia properties of large and complex structures and can be employed at an industrial level. 

  • 29.
    Aglert, Johan
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Digital feedback control of the frequency response of a conventional loudspeaker2004Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Automatic control design and Hi-Fi loudspeakers are two areas that not very often are combined. In 1976 Karl Erik Ståhl performed a master thesis project at KTH where he, with analog circuits, made a positive feedback loop to manipulate the mechanical parameters of a loudspeaker. That project introduced the idea to use control design when constructing loudspeakers. In this project this idea is pursued.

    For a subwoofer, the interesting thing from a control perspective is that it is the low frequency range that has to be controlled as opposed to the high frequency range which is normally the case in disturbance and servo problems. This master thesis project will present a solution to this problem where a digital signal processor is used to handle the feed back information. The IMC controller implemented in the processor is based on models derived from data, measured in the tailor made laboratory set-up that was built for the project. In order to satisfy the sampling rate requirements, the complexity of the control algorithm had to be restricted. Despite this limitation in the equipment, the frequency response of the loudspeaker was improved significantly at low frequencies.

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  • 30.
    Aguiar, Miguel
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Learning flow functions: architectures, universal approximation and applications to spiking systems2024Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Learning flow functions of continuous-time control systems is considered in this thesis. The flow function is the operator mapping initial states and control inputs to the state trajectories, and the problem is to find a suitable neural network architecture to learn this infinite-dimensional operator from measurements of state trajectories. The main motivation is the construction of continuous-time simulation models for such systems. The contribution is threefold.

    We first study the design of neural network architectures for this problem, when the control inputs have a certain discrete-time structure, inspired by the classes of control inputs commonly used in applications. We provide a mathematical formulation of the problem and show that, under the considered input class, the flow function can be represented exactly in discrete time. Based on this representation, we propose a discrete-time recurrent neural network architecture. We evaluate the architecture experimentally on data from models of two nonlinear oscillators, namely the Van der Pol oscillator and the FitzHugh-Nagumo oscillator. In both cases, we show that we can train models which closely reproduce the trajectories of the two systems.

    Secondly, we consider an application to spiking systems. Conductance-based models of biological neurons are the prototypical examples of this type of system. Because of their multi-timescale dynamics and high-frequency response, continuous-time representations which are efficient to simulate are desirable. We formulate a framework for surrogate modelling of spiking systems from trajectory data, based on learning the flow function of the system. The framework is demonstrated on data from models of a single biological neuron and of the interconnection of two neurons. The results show that we are able to accurately replicate the spiking behaviour.

    Finally, we prove an universal approximation theorem for the proposed recurrent neural network architecture. First, general conditions are given on the flow function and the control inputs which guarantee that the architecture is able to approximate the flow function of any control system with arbitrary accuracy. Then, we specialise to systems with dynamics given by a controlled ordinary differential equation, showing that the conditions are satisfied whenever the equation has a continuously differentiable right-hand side, for the control input classes of interest.

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  • 31.
    Aguiar, Miguel
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Das, Amritam
    Control Systems Group, Dept. of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Learning Flow Functions from Data with Applications to Nonlinear Oscillators2023In: 22nd IFAC World CongressYokohama, Japan, July 9-14, 2023, Elsevier BV , 2023, Vol. 56, p. 4088-4093Conference paper (Refereed)
    Abstract [en]

    We describe a recurrent neural network (RNN) based architecture to learn the flow function of a causal, time-invariant and continuous-time control system from trajectory data. By restricting the class of control inputs to piecewise constant functions, we show that learning the flow function is equivalent to learning the input-to-state map of a discrete-time dynamical system. This motivates the use of an RNN together with encoder and decoder networks which map the state of the system to the hidden state of the RNN and back. We show that the proposed architecture is able to approximate the flow function by exploiting the system's causality and time-invariance. The output of the learned flow function model can be queried at any time instant. We experimentally validate the proposed method using models of the Van der Pol and FitzHugh-Nagumo oscillators. In both cases, the results demonstrate that the architecture is able to closely reproduce the trajectories of these two systems. For the Van der Pol oscillator, we further show that the trained model generalises to the system's response with a prolonged prediction time horizon as well as control inputs outside the training distribution. For the FitzHugh-Nagumo oscillator, we show that the model accurately captures the input-dependent phenomena of excitability.

  • 32.
    Aguiar, Miguel
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Das, Amritam
    Eindhoven University of Technology, Control Systems Group, EE Dept., MB Eindhoven, The Netherlands.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Universal Approximation of Flows of Control Systems by Recurrent Neural Networks2023In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 2320-2327Conference paper (Refereed)
    Abstract [en]

    We consider the problem of approximating flow functions of continuous-time dynamical systems with inputs. It is well-known that continuous-time recurrent neural networks are universal approximators of this type of system. In this paper, we prove that an architecture based on discrete-time recurrent neural networks universally approximates flows of continuous-time dynamical systems with inputs. The required assumptions are shown to hold for systems whose dynamics are well-behaved ordinary differential equations and with practically relevant classes of input signals. This enables the use of off-the-shelf solutions for learning such flow functions in continuous-time from sampled trajectory data.

  • 33.
    Agüero, Juan C.
    et al.
    The University of Newcastle, Australia.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Goodwin, Graham C.
    The University of Newcastle, Australia.
    Fundamental Limitations on the Accuracy of MIMO Linear Models Obtained by PEM for Systems Operating in Open Loop2009In: Proceedings of the Joint 48th IEEE Conference on Decision and Control (CDC’09) and 28th Chinese Control Conference (CCC’09), 2009, p. 482-487Conference paper (Refereed)
    Abstract [en]

    In this paper we show that the variance of estimated parametric models for open loopMultiple-Input Multiple-Output (MIMO) systems obtained by the prediction error method (PEM) satisfies a fundamental integral limitation. The fundamental limitation gives rise to a multivariable 'water-bed' effect.

  • 34.
    Agüero, Juan C.
    et al.
    The University of Newcastle, Australia.
    Rojas, Cristian R.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Goodwin, Graham C.
    The University of Newcastle, Australia.
    Accuracy of linear multiple-input multiple-output (MIMO) models obtained by maximum likelihood estimation2012In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 48, no 4, p. 632-637Article in journal (Refereed)
    Abstract [en]

    In this paper, we study the accuracy of linear multiple-input multiple-output (MIMO) models obtained by maximum likelihood estimation. We present a frequency-domain representation for the information matrix for general linear MIMO models. We show that the variance of estimated parametric models for linear MIMO systems satisfies a fundamental integral trade-off. This trade-off is expressed as a multivariable 'water-bed' effect. An extension to spectral estimation is also discussed.

  • 35.
    Ahlberg, Sofie
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Human-in-the-Loop Control Synthesis for Multi-Agent Systems under Metric Interval Temporal Logic Specifications2019Licentiate thesis, monograph (Other academic)
    Abstract [en]

    With the increase of robotic presence in our homes and work environment, it has become imperative to consider human-in-the-loop systems when designing robotic controllers. This includes both a physical presence of humans as well as interaction on a decision and control level. One important aspect of this is to design controllers which are guaranteed to satisfy specified safety constraints. At the same time we must minimize the risk of not finding solutions, which would force the system to stop. This require some room for relaxation to be put on the specifications. Another aspect is to design the system to be adaptive to the human and its environment.

    In this thesis we approach the problem by considering control synthesis for multi-agent systems under hard and soft constraints, where the human has direct impact on how the soft constraint is violated. To handle the multi-agent structure we consider both a classical centralized automata based framework and a decentralized approach with collision avoidance. To handle soft constraints we introduce a novel metric; hybrid distance, which quantify the violation. The hybrid distance consists of two types of violation; continuous distance or missing deadlines, and discrete distance or spacial violation. These distances are weighed against each other with a weight constant we will denote as the human preference constant. For the human impact we consider two types of feedback; direct feedback on the violation in the form of determining the human preference constant, and direct control input through mixed-initiative control where the human preference constant is determined through an inverse reinforcement learning algorithm based on the suggested and followed paths. The methods are validated through simulations.

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  • 36.
    Ahlberg, Sofie
    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).
    Human in the Loop Least Violating Robot Control Synthesis under Metric Interval Temporal Logic Specifications2018In: 2018 European Control Conference, ECC 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 453-458, article id 8550179Conference paper (Refereed)
    Abstract [en]

    Recently, multiple frameworks for control synthesis under temporal logic have been suggested. The frameworks allow a user to give one or a set of robots high level tasks of different properties (e.g. temporal, time limited, individual and cooperative). However, the issue of how to handle tasks, which either seem to be or are infeasible, remains unsolved. In this paper we introduce a human to the loop, using the human's feedback to determine preference towards different types of violations of the tasks. We introduce a metric of violation called hybrid distance. We also suggest a novel framework for synthesizing a least violating controller with respect to the hybrid distance and the human feedback. Simulation result indicate that the suggested framework gives reasonable estimates of the metric, and that the suggested plans correspond to the expected ones.

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  • 37.
    Ahlberg, Sofie
    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).
    Human-in-the-loop control synthesis for multi-agent systems under hard and soft metric interval temporal logic specifications∗2019In: Proceedings 15th IEEE International Conference on Automation Science and Engineering, CASE 2019, IEEE Computer Society , 2019, p. 788-793Conference paper (Refereed)
    Abstract [en]

    In this paper we present a control synthesis framework for a multi-agent system under hard and soft constraints, which performs online re-planning to achieve collision avoidance and execution of the optimal path with respect to some human preference considering the type of the violation of the soft constraints. The human preference is indicated by a mixed initiative controller and the resulting change of trajectory is used by an inverse reinforcement learning based algorithm to improve the path which the affected agent tries to follow. A case study is presented to validate the result.

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  • 38.
    Ahlberg, Sofie
    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). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Mixed-Initiative Control Synthesis: Estimating an Unknown Task Based on Human Control Input2020In: Proceedings of the 3rd IFAC Workshop on Cyber-Physical & Human Systems,, 2020Conference paper (Refereed)
    Abstract [en]

    In this paper we consider a mobile platform controlled by two entities; an autonomousagent and a human user. The human aims for the mobile platform to complete a task, whichwe will denote as the human task, and will impose a control input accordingly, while not beingaware of any other tasks the system should or must execute. The autonomous agent will in turnplan its control input taking in consideration all safety requirements which must be met, sometask which should be completed as much as possible (denoted as the robot task), as well aswhat it believes the human task is based on previous human control input. A framework for theautonomous agent and a mixed initiative controller are designed to guarantee the satisfaction ofthe safety requirements while both the human and robot tasks are violated as little as possible.The framework includes an estimation algorithm of the human task which will improve witheach cycle, eventually converging to a task which is similar to the actual human task. Hence, theautonomous agent will eventually be able to find the optimal plan considering all tasks and thehuman will have no need to interfere again. The process is illustrated with a simulated example

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  • 39.
    Ahlén, Anders
    et al.
    Uppsala Univ, Signal Proc, Uppsala, Sweden.;Univ Newcastle, Dept Elect & Comp Engn, Callaghan, NSW, Australia..
    Åkerberg, Johan
    ABB Corp, Västerås, Sweden..
    Eriksson, Markus
    Scania CV, Södertalje, Sweden..
    Isaksson, Alf J.
    Linköping Univ, Linköping, Sweden.;Univ Newcastle, Callaghan, NSW, Australia.;Royal Inst Technol, Stockholm, Sweden.;ABB Corp Res, Vasteras, Sweden..
    Iwaki, Takuya
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). JGC Corp, Yokohama, Kanagawa, Japan.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Knorn, Steffi
    Univ Newcastle, Ctr Complex Dynam Syst & Control, Callaghan, NSW, Australia.;Uppsala Univ, Signals & Syst Div, Uppsala, Sweden..
    Lindh, Thomas
    Iggesund Mill, Maintenance Technol Dev, Iggesund Paperboard, Sweden..
    Sandberg, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). CALTECH, Pasadena, CA 91125 USA.;MIT, Lab Informat & Decis Syst, 77 Massachusetts Ave, Cambridge, MA 02139 USA..
    Toward Wireless Control in Industrial Process Automation: A Case Study at a Paper Mill2019In: IEEE Control Systems Magazine, ISSN 1066-033X, Vol. 39, no 5, p. 36-57Article in journal (Refereed)
    Abstract [en]

    Wireless sensors and networks are used only occasionally in current control loops in the process industry. With rapid developments in embedded and highperformance computing, wireless communication, and cloud technology, drastic changes in the architecture and operation of industrial automation systems seem more likely than ever. These changes are driven by ever-growing demands on production quality and flexibility. However, as discussed in "Summary," there are several research obstacles to overcome. The radio communication environment in the process industry is often troublesome, as the environment is frequently cluttered with large metal objects, moving machines and vehicles, and processes emitting radio disturbances [1], [2]. The successful deployment of a wireless control system in such an environment requires careful design of communication links and network protocols as well as robust and reconfigurable control algorithms.

  • 40.
    Akbarnejad, Shahin
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Applied Process Metallurgy.
    Kennedy, M. W.
    Fritzsch, R.
    Aune, R. E.
    An investigation on permeability of ceramic foam filters (CFF)2015In: TMS Light Metals, 2015, p. 949-954Conference paper (Refereed)
    Abstract [en]

    CFFs are used to filter liquid metal in the aluminum industry. CFFs are classified in grades or pores per inch (PPI), ranging from 10-100 PPI. Their properties vary in everything from pore and strut size to window size. CFFs of 80-100 PPI are generally not practical for use by industry, as priming of the filters by gravitational forces requires an excessive metal head. Recently, co-authors have invented a method to prime such filters using electromagnetic Lorentz forces, thus allowing filters to be primed with a low metal head. In the continuation of this research work, an improved experimental setup was developed in the present study to validate previous results and to measure the permeability of different filters, as well as a stack of filters. The study of permeability facilitates estimation of the required pressure drop to prime the filters and the head required to generate a given casting rate.

  • 41. Akcay, H.
    et al.
    Hjalmarsson, Håkan
    Department of Electrical Engineering, Linköping University.
    Ljung, Lennart
    Department of Electrical Engineering, Linköping University.
    On the choice of norms in system identification1996In: IEEE Transactions on Automatic Control, ISSN 0018-9286, Vol. 41, no 9, p. 1367-1372Article in journal (Refereed)
    Abstract [en]

    In this paper we discuss smooth and sensitive norms for prediction error system identification when the disturbances are magnitude bounded. Formal conditions for sensitive norms, which give an order of magnitude faster convergence of the parameter estimate variance, are developed. However, it also is shown that the parameter estimate variance convergence rate of sensitive norms is arbitrarily bad for certain distributions. A necessary condition for a norm to be statistically robust with respect to the family F(C) of distributions with support [-C, C] for some arbitrary C > 0 is that its second derivative does not vanish on the support. A direct consequence of this observation is that the quadratic norm is statistically robust among all ℓp-norms, p ≀ 2 < ∞ for F(C). ©1996 IEEE.

  • 42.
    Akçay, H.
    et al.
    Linköping University.
    Hjalmarsson, Håkan
    Linköping University.
    The Least-Squares Identification of FIR Systems Subject to Worst-Case Noise1994In: 10th IFAC Symposium on System Identification, 1994, Vol. 2, p. 85-90Conference paper (Refereed)
  • 43.
    Akçay, H.
    et al.
    Linköping University.
    Hjalmarsson, Håkan
    Linköping University.
    The least-squares identification of FIR systems subject to worst-case noise1994In: System & Control Letters, Vol. 23, p. 329-338Article in journal (Refereed)
  • 44.
    Akçay, H.
    et al.
    Linköping University.
    Hjalmarsson, Håkan
    Linköping University.
    Ljung, Lennart
    Department of Electrical Engineering, Linköping University.
    On the choice of norms i system identification1994In: 10th IFAC Symposium on System Identification, 1994, Vol. 2, p. 103-108Conference paper (Refereed)
  • 45.
    Al Alam, Assad
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Optimally Fuel Ecient Speed Adaptation2008Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    An optimal velocity trajectory for a heavy duty vehicle, obtained with the aid of modern GPS and digital map devices, depends on several variables. Curvature speed limitations, road grade, and posted road speed are common constraints imposed by the road travelled. This thesis presents a method for modelling and analysing a switching controller through the use of the former mentioned constraints. A non-linear model for the heavy duty vehicle is derived, enabling suitable control methods to be applied. Pontryagin’s Principal and LQR are discussed to get a profound understanding of how the controller should be designed. It is discovered that a switching controller based on optimal control and engineering experience is most favourable for the problem at hand. The controller is designed to address the main objectives set in this paper of minimising fuel consumption, travelling time, and brake wear.

    Gauss-Newtons’s algorithm for non-linear equations is used to estimate curve radii. Other input parameters are presumed to be available. GPS data error is discussed to perform a sensitivity analysis. An electronic horizon is produced on three road segments, entailed with data of the future road topology. Finally the switching controller is applied to the road segments. Experimental results show that the controller produces a velocity trajectory, which reduces fuel consumption by 5-15% and brake wear by 15-35%, while the travelling time is only increased by 1-2%.

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  • 46.
    Al Alam, Assad
    et al.
    Scania CV AB.
    Gattami, Ather
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    An experimental study on the fuel reduction potential of heavy duty vehicle platooning2010In: 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2010, IEEE , 2010, p. 306-311Conference paper (Refereed)
    Abstract [en]

    Vehicle platooning has become important for the vehicle industry. Yet conclusive results with respect to the fuel reduction possibilities of platooning remain unclear. The focus in this study is the fuel reduction that heavy duty vehicle platooning enables and the analysis with respect to the influence of a commercial adaptive cruise control on the fuel consumption. Experimental results show that by using preview information of the road ahead from the lead vehicle, the adaptive cruise controller can reduce the fuel consumption. A study is undertaken for various masses of the lead vehicle. The results show that the best choice with respect to a heavier or lighter lead vehicle depends on the desired time gap. A maximum fuel reduction of 4.7-7.7% depending on the time gap, at a set speed of 70 km/h, can be obtained with two identical trucks. If the lead vehicle is 10 t lighter a corresponding 3.8-7.4% fuel reduction can be obtained depending on the time gap. Similarly if the lead vehicle is 10 t heavier a 4.3-6.9% fuel reduction can be obtained. All results indicate that a maximum fuel reduction can be achieved at a short relative distance, due to both air drag reduction and suitable control.

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  • 47.
    Al Alam, Assad
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Gattami, Ather
    Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley CA, 94720-1770, United States .
    Johansson, Karl Henrik
    Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley CA, 94720-1770, United States .
    Tomlin, Claire Jennifer
    Scania CV AB, Södertälje, Sweden.
    Establishing safety for heavy duty vehicle platooning: a game theoretical approach2011In: IFAC Proceedings Volumes (IFAC-PapersOnline), 2011, p. 3818-3823Conference paper (Refereed)
    Abstract [en]

    It is fuel efficient to minimize the relative distance between vehicles to achievea maximum reduction in air drag. However, the relative distance can only be reduced to acertain extent without endangering a collision. Factors such as the vehicle velocity, the relativevelocity, and the characteristics of the vehicle ahead has a strong impact on what minimumrelative distance can be obtained. In this paper, we utilize optimal control and game theory toestablish safety criteria for heavy duty vehicle platooning applications. The derived results showthat a minimum relative distance of 1.2m can be obtained for two identical vehicles withoutendangering a collision, assuming that there is no delay present in the feedback system. If aworst case delay is present in the system, a minimum relative distance is deduced based uponthe vehicle’s maximum deceleration ability. The relative distance can be reduced if the followervehicle has a greater overall braking capability, which suggests that vehicle heterogeneity andorder has substantial impact. The findings are verified by simulations and the main conclusion isthat the relative distance utilized in commercial applications today can be reduced significantlywith a suitable advanced cruise control system.

  • 48.
    Al Marjani, Aymen
    et al.
    ENS Lyon, UMPA, Lyon, France..
    Proutiere, Alexandre
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Adaptive Sampling for Best Policy Identification in Markov Decision Processes2021In: Proceedings of the 38 th International Conference on Machine Learning, PMLR 139, 2021 / [ed] Meila, M Zhang, T, The Journal of Machine Learning Research (JMLR) , 2021, Vol. 139Conference paper (Refereed)
    Abstract [en]

    We investigate the problem of best-policy identification in discounted Markov Decision Processes (MDPs) when the learner has access to a generative model. The objective is to devise a learning algorithm returning the best policy as early as possible. We first derive a problem-specific lower bound of the sample complexity satisfied by any learning algorithm. This lower bound corresponds to an optimal sample allocation that solves a non-convex program, and hence, is hard to exploit in the design of efficient algorithms. We then provide a simple and tight upper bound of the sample complexity lower bound, whose corresponding nearly-optimal sample allocation becomes explicit. The upper bound depends on specific functionals of the MDP such as the sub-optimality gaps and the variance of the next-state value function, and thus really captures the hardness of the MDP. Finally, we devise KLB-TS (KL Ball Track-and-Stop), an algorithm tracking this nearly-optimal allocation, and provide asymptotic guarantees for its sample complexity (both almost surely and in expectation). The advantages of KLB -TS against state-of-the-art algorithms are discussed and illustrated numerically.

  • 49.
    Aladele, Victor
    et al.
    Georgia Inst Technol, Dept Elect & Comp Engn, Atlanta, GA 30318 USA..
    Rodriguez de Cos, Carlos
    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).
    Hutchinson, Seth
    Georgia Inst Technol, Dept Elect & Comp Engn, Atlanta, GA 30318 USA..
    An Adaptive Cooperative Manipulation Control Framework for Multi-Agent Disturbance Rejection2022In: 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 100-106Conference paper (Refereed)
    Abstract [en]

    The success of a cooperative manipulation process depends on the level of disturbance rejection between the cooperating agents. However, this attribute may be jeopardized due to unexpected behaviors, such as joint saturation or internal collisions. This leads to deterioration in the performance of the manipulation task. In this paper, we present an adaptive distributed control framework that directly mitigates these internal disturbances, both in the joint (and task) spaces. With our approach, we show that including the manipulator-load coupling in the definition of the task error yields improved performance and robustness. To validate this statement, we provide stability guarantees and simulation results for two implementation cases.

  • 50.
    Alam, Assad
    KTH, School of Electrical Engineering (EES).
    Fuel-Efficient Distributed Control for Heavy Duty Vehicle Platooning2011Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Freight transport demand has escalated and will continue to do so as economiesgrow. As the traffic intensity increases, the drivers are faced with increasinglycomplex tasks and traffic safety is a growing issue. Simultaneously, fossil fuel usageis escalating. Heavy duty vehicle (HDV) platooning is a plausible solution to theseissues. Even though there has been a need for introducing automated HDV platooningsystems for several years, they have only recently become possible to implement.Advancements in on-board and external technology have ushered in new possibilitiesto aid the driver and enhance the system performance. Each vehicle is able to serveas an information node through wireless communication; enabling a cooperativenetworked transportation system. Thereby, vehicles can semi-autonomously travel atshort intermediate spacings, effectively reducing congestion, relieving driver tension,improving fuel consumption and emissions without compromising safety.

    This thesis presents contributions to a framework for the design and implementation of HDV platooning. The focus lies mainly on establishing and validating realconstraints for fuel optimal control for platooning vehicles. Nonlinear and linearvehicle models are presented together with a system architecture, which dividesthe complex problem into manageable subsystems. The fuel reduction potentialis investigated through simulation models and experimental results derived fromstandard vehicles traveling on a Swedish highway. It is shown through analyticaland experimental results that it is favorable with respect to the fuel consumption tooperate the vehicles at a much shorter intermediate spacing than what is currentlydone in commercially available systems. The results show that a maximum fuelreduction of 4.7–7.7 % depending on the inter-vehicle time gap, at a set speedof 70 km/h, can be obtained without compromising safety. A systematic designmethodology for inter-vehicle distance control is presented based on linear quadraticregulators (LQRs). The structure of the controller feedback matrix can be tailoredto the locally available state information. The results show that a decentralizedcontroller gives good tracking performance, a robust system and lowers the controleffort downstream in the platoon. It is also shown that the design methodologyproduces a string stable system for an arbitrary number of vehicles in the platoon,if the vehicle configurations and the LQR weighting parameters are identical for theconsidered subsystems.

    With the results obtained in this thesis, it is argued that a vast fuel reductionpotential exists for HDV platooning. Present commercial systems can be enhancedsignificantly through the introduction of wireless communication and decentralizedoptimal control.

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