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arXiv 2021年

作者： Tafat, Rania Göhrt, Thomas Streif, Stefan Technische Universität Chemnitz Chemnitz09126 Germany Automatic Control and System Dynamics Lab Germany

Converse optimality theory addresses an optimal control problem conversely where the system is unknown and the value function is chosen. Previous work treated this problem both in continuous and discrete time and non-extensively considered disturbances. In this paper, the converse optimality theory is extended to the class of affine systems with disturbances in continuous time while considering norm constraints on both control inputs and disturbances. The admissibility theorem and the design of the internal dynamics model are generalized in this context. A robust stabilizability condition is added for the initial converse optimality problem using inverse optimality’s tool: the robust control Lyapunov function. A design for nonlinear class of systems that are both robustly stabilizable and globally asymptotically stable in open loop is obtained. A case study illustrates the presented theory. © 2021, CC BY-NC-ND.

关键词： Robust control

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Nonlinear Optimal control of Traffic Flow with Stability Guarantees

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IFAC-PapersOnLine 2023年第2期56卷 4965-4970页

作者： Urs Baumgart Francisco Moreno-Mora Lukas Beckenbach Michael Burger Stefan Streif Fraunhofer Institute for Industrial Mathematics ITWM Kaiserslautern 67663 Germany Technische Universität Chemnitz Chemnitz 09126 Germany Automatic Control and System Dynamics Lab

Automated vehicles have been proposed as a way to influence traffic flow to avoid congestion and maintain a smooth traffic flow. Experiments have shown that congestion formation can be reproduced in an artificial ring road scenario. We design a model predictive controller for the ring road system assuming heterogeneous drivers and an automated vehicle for which congestion resolution and convergence to a reference speed can be shown. The driver's model captures human driving responses. A stabilizing predictive controller framework is employed under the use of a local controllability assumption connected to a local linear quadratic regulator. A case study shows the efficacy of the proposed controller and provides numerical values for the required prediction horizon, highlighting congestion resolution as well as theoretical conservativeness.

关键词： Nonlinear and optimal automotive control Multi-vehicle systems Predictive control Stability Autonomous vehicles

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Model Predictive Purge control for PEM Fuel Cell systems with Anode Recirculation

Model Predictive Purge Control for PEM Fuel Cell Systems wit...

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IEEE Conference on Decision and control

作者： Michael Hauck Felix Petzke Stefan Streif Faculty for Electrical Engineering and Information Technology Automatic Control & System Dynamics Lab at Technische Universität Chemnitz Chemnitz Germany

ISBN: (纸本)9781665436601

Fuel cell systems that utilize anode recirculation generally require a purge process to remove accumulated gaseous impurities from the anode recirculation system. Especially the accumulation of nitrogen leads to a decrease of the cell voltage and therefore a reduced stack efficiency. However, unconsumed hydrogen is lost during the purge process, resulting in a decrease of hydrogen utilization. Therefore, an optimal purge control can help to maximize the overall system *** order to determine and predict the influence of the purge valve opening on the system efficiency with respect to the hydrogen utilization and the stack efficiency we develop a control-oriented model of a PEMFC anode recirculation system. We then set up a model predictive purge controller and compare its performance to two standard purge strategies using the NEDC vehicle test cycle.

关键词： Impurities Conferences Hydrogen Fuel cells Predictive models Valves Nitrogen

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A Hierarchical MPC Scheme for Ensembles of Hammerstein systems

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IFAC-PapersOnLine 2020年第2期53卷 6951-6956页

作者： Felix Petzke Marcello Farina Stefan Streif Technische Universität Chemnitz Automatic Control & System Dynamics Lab Chemnitz Germany Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy

Hierarchical control approaches have been one of the elective methods for the optimal control of large-scale systems in the last decades. In (Petzke et al., 2018) we presented a multirate hierarchical MPC scheme for linear systems, with remarkable flexibility and scalability properties. In this paper we extend the former approach to ensembles of Hammerstein systems and we complement the method by proposing a suitable high-level optimizer. The theoretical properties are discussed in the light of the theoretical properties of the former method. Lastly, an example case study is presented to show the effectiveness of the proposed method.

关键词： Hierarchical control model predictive control Hammerstein systems

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PoCET: a Polynomial Chaos Expansion Toolbox for Matlab

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IFAC-PapersOnLine 2020年第2期53卷 7256-7261页

作者： Felix Petzke Ali Mesbah Stefan Streif Automatic Control & System Dynamics Lab at Technische Universität Chemnitz Faculty for Electrical Engineering and Information Technology Chemnitz Germany Department of Chemical and Biomolecular Engineering University of California Berkeley CA USA

We introduce PoCET: a free and open-scource Polynomial Chaos Expansion Toolbox for Matlab, featuring the automatic generation of polynomial chaos expansion (PCE) for linear and nonlinear dynamic systems with time-invariant stochastic parameters or initial conditions, as well as several simulation tools. It offers a built-in handling of Gaussian, uniform, and beta probability density functions, projection and collocation-based calculation of PCE coefficients, and the calculation of stochastic moments from a PCE. Efficient algorithms for the calculation of the involved integrals have been designed in order to increase its applicability. PoCET comes with a variety of introductory and instructive examples. Throughout the paper we show how to perform a polynomial chaos expansion on a simple ordinary differential equation using PoCET, as well as how it can be used to solve the more complex task of optimal experimental design.

关键词： polynomial chaos simulation tools optimal experiment design optimal control

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Reserve Balancing in a Microgrid system for Safety Analysis ⁎

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IFAC-PapersOnLine 2020年第2期53卷 13581-13586页

作者： Clemens Kiebler Ionela Prodan Felix Petzke Stefan Streif Technische Universität Chemnitz Automatic Control and System Dynamics Lab Univ. Grenoble Alpes Grenoble INP Institute of Engineering Univ. Grenoble Alpes. LCIS F-26000 Valence

This work presents a robust MPC (Model Predictive control) approach for reserve balancing in DC microgrid systems under uncertainties like wind power and energy price variations and different types of fault events. The robust MPC algorithm considers a variable-length prediction horizon which accounts for forecasts in energy price and renewable power over one day. Furthermore, a storage system is used to increase the utility of the demands and minimize the energy costs. The algorithm is tested for multiple fault types which affect the system (line and loss of power faults).

关键词： Energy management systems DC microgrid Fault events Distributed energy resources Robust MPC (Model Predictive control)

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Deep Neural Network Based Optimal control of Greenhouses

Deep Neural Network Based Optimal Control of Greenhouses

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European control Conference (ECC)

作者： Kiran Kumar Sathyanarayanan Philipp Sauerteig Stefan Streif Automatic Control and System Dynamics Lab Technische Universität Chemnitz Chemnitz Germany Department of Bioresources Fraunhofer Institute for Molecular Biology and Applied Ecology Giessen Germany

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

automatic control of greenhouse crop production is of great interest owing to the increasing energy and labor costs. In this work, we use two-level control, where the upper level generates suitable reference trajectories for states and control inputs based on day-ahead predictions. These references are tracked in the lower level using Nonlinear Model Predictive control (NMPC). In order to apply NMPC, a model of the greenhouse dynamics is essential. However, the complex nature of the underlying model including discontinuities and nonlinearities results in intractable computational complexity and long sampling times. As a remedy, we employ NMPC as a data generator to learn the tracking control policy using deep neural networks. Then, the references are tracked using the trained Deep Neural Network (DNN) to reduce the computational burden. The efficiency of our approach under real-time disturbances is demonstrated by means of a simulation study.

关键词： Power demand Computational modeling Memory management Optimal control Artificial neural networks Production Real-time systems

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Deep Neural Network Based Optimal control of Greenhouses

arXiv

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arXiv 2023年

作者： Sathyanarayanan, Kiran Kumar Sauerteig, Philipp Streif, Stefan Technische Universität Chemnitz Automatic Control and System Dynamics Lab Chemnitz09126 Germany Fraunhofer Institute for Molecular Biology and Applied Ecology Department of Bioresources Giessen Germany

关键词： Deep neural networks

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COMPUTER-ASSISTED PROOFS FOR LYAPUNOV STABILITY VIA SUMS OF SQUARES CERTIFICATES AND CONSTRUCTIVE ANALYSIS

arXiv

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arXiv 2020年

作者： Devadze, Grigory Magron, Victor Streif, Stefan Technische Universität Chemnitz Automatic Control and System Dynamics Lab Chemnitz09107 Germany LAAS 7 avenue du Colonel Roche Toulouse31077 Cedex 4 France

We provide a computer-assisted approach to ensure that a given discrete-time polynomial system is (asymptotically) stable. Our framework relies on constructive analysis together with formally certified sums of squares Lyapunov functions. The crucial steps are formalized within the proof assistant Minlog. We illustrate our approach with various examples issued from the control system literature. Copyright © 2020, The Authors. All rights reserved.

关键词： Lyapunov functions

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On inf-convolution-based robust practical stabilization under computational uncertainty

arXiv

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arXiv 2021年

作者： Schmidt, Patrick Osinenko, Pavel Streif, Stefan Technische Universität Chemnitz Automatic Control and System Dynamics Lab Chemnitz09126 Germany Computational and Data Science and Engineering Center Skolkovo Institute of Science and Technology Moscow143026 Russia

This work is concerned with practical stabilization of nonlinear systems by means of inf-convolution-based sample-and-hold control. It is a fairly general stabilization technique based on a generic non-smooth control Lyapunov function (CLF) and robust to actuator uncertainty, measurement noise, etc. The stabilization technique itself involves computation of descent directions of the CLF. It turns out that non-exact realization of this computation leads not just to a quantitative, but also qualitative obstruction in the sense that the result of the computation might fail to be a descent direction altogether and there is also no straightforward way to relate it to a descent direction. Disturbance, primarily measurement noise, complicate the described issue even more. This work suggests a modified inf-convolution-based control that is robust w. r. t. system and measurement noise, as well as computational uncertainty. The assumptions on the CLF are mild, as, e. g., any piece-wise smooth function, which often results from a numerical LF/CLF construction, satisfies them. A computational study with a three-wheel robot with dynamical steering and throttle under various tolerances w. r. t. computational uncertainty demonstrates the relevance of the addressed issue and the necessity of modifying the used stabilization technique. Similar analyses may be extended to other methods which involve optimization, such as Dini aiming or steepest descent. © 2021, CC BY-NC-ND.

关键词： Nonlinear systems

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