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63rd IEEE Conference on Decision and control, CDC 2024

作者： Stoican, Florin Mihai, Stefan S. Monnigmann, Martin Ciubotaru, Bogdan D. University 'Politehnica' of Bucharest Department of Automatic Control and Systems Engineering 313 Splaiul Independenţei Sector 6 Bucharest060042 Romania Ruhr University Bochum Martin Mönnigmann Is with Automatic Control and System Theory Bochum44801 Germany

ISBN: (纸本)9798350316339

Solving the explicit model predictive control (MPC) problem entails enumerating a list of critical regions and their ancillary feedback laws. Unfortunately, their number and the time required to compute them increase exponentially with the problem size (state-space model dimension and length of the prediction horizon). We show that when, as it is often the case, the problem's constraints take the form of boxes or zonotopes, the resulting feasible domain can be compactly described as a constrained zonotope. Subsequently, we investigate whether, and under which circumstances, the combinatorial structure of the constrained zonotope interpretation accelerates the computation of the explicit solution. © 2024 IEEE.

关键词： State space methods

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Embedded Implementation of a Neural Network emulating Nonlinear MPC in a process control application*

Embedded Implementation of a Neural Network emulating Nonlin...

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European control Conference (ECC)

作者： Sebastian Leonow Raphael Dyrska Martin Mönnigmann Department of Mechanical Engineering Automatic Control and System Theory Ruhr-Universität Bochum Bochum Germany

We present the design, training, and implementation of a nonlinear autoregressive neural network for the control of a multi-input, multi-output hydraulic plant. The network mimics the optimal control signals of a nonlinear model predictive controller and is implemented on a low-level microcontroller. While trained with simulation data only, experiments on the real plant show that not only the setpoint tracking, but to some degree also the constraint satisfaction and unmeasured disturbance rejection are adapted by the neural network. In contrast to the optimization-based predictive controller, the neural network easily runs on an ESP32 microcontroller and Micropython with guaranteed evaluation time and still achieves similar control performance as the predictive controller.

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Computing the explicit MPC solution in the constrained zonotope case

Computing the explicit MPC solution in the constrained zonot...

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IEEE Conference on Decision and control

作者： Florin Stoican Ştefan S. Mihai Martin Mönnigmann Bogdan D. Ciubotaru Department of Automatic Control and Systems Engineering University “Politehnica” of Bucharest 313 Splaiul Independenţei 060042 Sector 6 Bucharest Romania Martin Mönnigmann is with Automatic Control and System Theory Ruhr University Bochum Bochum Germany

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Solving the explicit model predictive control (MPC) problem entails enumerating a list of critical regions and their ancillary feedback laws. Unfortunately, their number and the time required to compute them increase exponentially with the problem size (state-space model dimension and length of the prediction horizon). We show that when, as it is often the case, the problem’s constraints take the form of boxes or zonotopes, the resulting feasible domain can be compactly described as a constrained zonotope. Subsequently, we investigate whether, and under which circumstances, the combinatorial structure of the constrained zonotope interpretation accelerates the computation of the explicit solution.

关键词： State-space methods Predictive control

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Derivative-Free Data-Driven control of Continuous-Time Linear Time-Invariant systems

arXiv

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arXiv 2024年

作者： Bosso, Alessandro Borghesi, Marco Iannelli, Andrea Notarstefano, Giuseppe Teel, Andrew R. Department of Electrical Electronic and Information Engineering University of Bologna Italy Institute for System Theory and Automatic Control University of Stuttgart Germany Department of Electrical and Computer Engineering University of California Santa BarbaraCA United States

This paper develops a data-driven stabilization method for continuous-time linear time-invariant systems with theoretical guarantees and no need for signal derivatives. The framework, based on linear matrix inequalities (LMIs), is illustrated in the state-feedback and single-input single-output output-feedback scenarios. Similar to discrete-time approaches, we rely solely on input and state/output measurements. To avoid differentiation, we employ low-pass filters of the available signals that, rather than approximating the derivatives, reconstruct a non-minimal realization of the plant. With access to the filter states and their derivatives, we can solve LMIs derived from sample batches of the available signals to compute a dynamic controller that stabilizes the plant. The effectiveness of the framework is showcased through numerical examples. © 2024, CC BY.

关键词： Continuous time systems

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Model-Based and Data-Driven control of Event- and Self-Triggered Discrete-Time LTI systems

arXiv

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arXiv 2022年

作者： Wang, Xin Berberich, Julian Sun, Jian Wang, Gang Allgöwer, Frank Chen, Jie The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

The present paper considers the model-based and data-driven control of unknown linear time-invariant discretetime systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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Constrained extremum seeking: A modified-barrier function approach 11

Constrained extremum seeking: A modified-barrier function ap...

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11th IFAC Symposium on Nonlinear control systems, NOLCOS 2019

作者： Labar, Christophe Garone, Emanuele Kinnaert, Michel Ebenbauer, Christian Department of Control Engineering and System Analysis Université libre de Bruxelles Brussels1050 Belgium Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart70569 Germany

In this paper, we address the problem of steering the input of a convex function to a value that minimizes the function under a convex constraint. We consider the case where the constraint cannot be violated of more than a user-defined value during the whole transient phase. The mathematical expression of both the cost function and the constraint are assumed to be unknown. The only information available are the on-line values of the cost and the constraint. To tackle this problem, an optimization law, based on a modified-barrier function, and involving the gradient of both the cost function and the constraint, is firstly designed. The Lie bracket formalism is then exploited to approximate this law, by combining time-periodic signals with the on-line measurements of both the cost and the constraint. The stability property of the resulting constrained extremum seeking system is proved, and its effectiveness is shown in simulation. © 2019, IFAC (International Federation of automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Cost functions

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Constrained Extremum Seeking: a Modified-Barrier Function Approach ⁎

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IFAC-PapersOnLine 2019年第16期52卷 694-699页

作者： Christophe Labar Emanuele Garone Michel Kinnaert Christian Ebenbauer Department of Control Engineering and System Analysis Université libre de Bruxelles 1050 Brussels Belgium Institute for Systems Theory and Automatic Control University of Stuttgart 70569 Stuttgart Germany

关键词： Constrained Optimization Extremum Seeking Singular Perturbations Data-based control Modified Barrier-Function

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Nash optimality based distributed model predictive control for vehicle platoon ⁎

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IFAC-PapersOnLine 2020年第2期53卷 6610-6615页

作者： Shuyou Yu Hao Chen Yangyang Feng Yajing Zhang Yongfu Li Christian Ebenbauer Hong Chen Department of Control Science and Engineering Jilin University Changchun 130012 P.R. China College of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 P.R. China Institute for System Theory and Automatic Control University of Stuttgart Stuttgart 70550 Germany Clean Energy Automotive Engineering Tongji University Shanghai 201804 P. R. China

In this paper, a distributed model predictive control algorithm (DMPC) based on Nash optimality is proposed for automated vehicle platoon control. The optimization decision of vehicle platoon is decomposed into the decentralized optimization of single vehicles, in which the Nash optimality algorithm is adopted to solve the decentralized optimization problem. Thus, each vehicle can reach the local optimal target and the whole team can reach its Nash equilibrium. The methodology employs neighborhood information of the entire platoon through on-board sensors and V2V communication to achieve coordination of the entire platoon. The ability of the methods in terms of robustness to disturbances and cyber-physical interaction is demonstrated with simulation case studies.

关键词： Vehicle platoon Distributed control Model predictive control Nash optimality

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Extremum seeking algorithms based on non-commutative maps 11

Extremum seeking algorithms based on non-commutative maps

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11th IFAC Symposium on Nonlinear control systems, NOLCOS 2019

作者： Feiling, Jan Labar, Christophe Grushkovskaya, Victoria Garone, Emanuele Kinnaert, Michel Ebenbauer, Christian Institute for Systems Theory and Automatic Control University of Stuttgart Germany Dr. Ing. h.c. F. Porsche AG Stuttgart Germany Department of Control Engineering and System Analysis Université libre de Bruxelles Belgium Institute for Mathematics Julius-Maximilians-University Würzburg Germany

In this work, discrete-time extremum seeking algorithms for unconstrained optimization problems are developed. A general class of non-commutative maps and one- and two point function evaluation polices are presented to approximate a gradient-descent algorithm, suitable for extremum seeking problems. Moreover, adaptive step size rules are discussed to achieve faster convergence and vanishing steady state oscillations. © 2019, IFAC (International Federation of automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Optimization

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Extremum Seeking Algorithms based on Non-Commutative Maps

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IFAC-PapersOnLine 2019年第16期52卷 688-693页

作者： Jan Feiling Christophe Labar Victoria Grushkovskaya Emanuele Garone Michel Kinnaert Christian Ebenbauer Institute for Systems Theory and Automatic Control University of Stuttgart Germany Dr. Ing. h.c. F. Porsche AG Stuttgart Germany Department of Control Engineering and System Analysis Université libre de Bruxelles Belgium Institute for Mathematics Julius-Maximilians-University Würzburg Germany

关键词： Extremum Seeking Optimization Adaptive control Discrete-Time systems

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