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第41届中国控制会议

作者： Yang San Fan Zhang Changxin Lu Deyuan Meng Kaiquan Cai The Seventh Research Division Beihang University (BUAA) School of Automation Science and Electrical Engineering Beihang University (BUAA) School of Electronics and Information Engineering Beihang University (BUAA)

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

For the adaptive tracking of nonaffine nonlinear systems,the unknown nonlinear dynamics is regarded as one of the essential factors that may hinder the effective controller establishment and the tracking precision *** tackle this difficulty for a class of single-input single-output(SISO) nonaffine nonlinear systems,we develop an optimization-based design method for data-driven control to achieve the desired tracking objective in the presence of the time-varying reference *** is shown that the proposed data-driven controller can render the tracking error to strictly converge,regardless of the SISO unknown nonlinear *** addition,thanks to a novel grouping-based contraction mapping method and properties of nonnegative matrices,the boundedness of the I/O sequence is *** are implemented to verify that the proposed data-driven controller can realize the anticipated tracking performance for nonlinear systems.

关键词： Nonaffine nonlinear system data-driven control Grouping-based analysis method Non-negative matrix

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Periodic event-triggered data-driven control for networked control systems with time-varying delays

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SYSTEMS & control LETTERS 2024年 193卷

作者： Wei, Zi-Jie Liu, Kun-Zhi Wang, Yan-Wei Pan, Zhuo-Rui Wen, Si-Xin Sun, Xi-Ming Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Aviat Ind Corp China Ltd Shenyang Aircraft Design & Res Inst Shenyang 110035 Peoples R China

This article focuses on data-driven analysis and controller design for networked control systems (NCSs) with network-induced delays. The study considers a linear time-invariant (LTI) system controlled through a periodic event-triggering mechanism. First, by leveraging data-based representations, we establish data-based stability conditions for NCSs with time-varying delays. Furthermore, we propose the data-based method for co-designing the controller and the periodic event-triggering scheme. In addition, we present novel data-based conditions for verifying dissipativity properties of NCSs. The effectiveness of our proposed methods is validated through a simulation and a turbofan engine hardware-in-the-loop (HIL) experiment.

关键词： data-driven control Periodic event-triggered control Networked control systems Time-varying delays Dissipativity

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A Dynamic Linearization Modeling of Thermally Induced Error Based on data-driven control for CNC Machine Tools

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2021年第2期22卷 241-258页

作者： Liu, Puling Yao, Xiaodong Ge, Guangyan Du, Zhengchun Feng, Xiaobing Yang, Jianguo Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China

This paper proposes a novel dynamic linearization modeling method for machine tool thermal errors based on data-driven control theory, with improved accuracy and robustness under various practical working conditions of machine tool. The nonlinear, quasi-static and pseudo-hysteric characteristics of the machine tool temperature field are identified as the main causes for poor robustness in conventional thermal error mathematical models. The theoretical and practical difficulties in applying conventional modeling approaches based on the model-based control theory are demonstrated using two types of CNC machine tools as examples. The data-driven control theory is applied to dynamic linearization modeling and the developed data model has shown significant improvement over the general dynamic model in terms of model accuracy and robustness. The feasibility and effectiveness of the proposed dynamic linearization modeling method has been verified using two experiments, demonstrating excellent robustness and ability to adapt to various machining conditions and to improve machining accuracy.

关键词： CNC machine tool Thermal error Dynamic modeling data-driven control data model

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Optimal direct data-driven control with stability guarantees

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EUROPEAN JOURNAL OF control 2021年 59卷 175-187页

作者： Selvi, Daniela Piga, Dario Battistelli, Giorgio Bemporad, Alberto Univ Firenze Dipartimento Ingn Ind DIEF Via Santa Marta 3 I-50139 Florence Italy IDSIA Dalle Molle Inst Artificial Intelligence SUPSI USI CH-6928 Manno Switzerland Univ Firenze Dipartimento Ingn Informaz DINFO Via Santa Marta 3 I-50139 Florence Italy IMT Sch Adv Studies Lucca Piazza San Francesco 19 I-55100 Lucca Italy

For model-free optimal control design, this paper proposes an approach based on optimizing the reference model that is used in direct data-driven controller synthesis. Optimality is defined with respect to suitable cost functions reflecting desired performance and control objectives. We rely on the well-known Virtual Reference Feedback Tuning technique and on a direct control design approach that ensures stability of the resulting closed-loop system. The proposed design method leads to a non-convex optimization problem with a small number of variables that can be easily solved by a global optimizer, such as by particle swarm optimization. The effectiveness of the proposed solution is illustrated in simulation examples. (c) 2020 European control Association. Published by Elsevier Ltd. All rights reserved.

关键词： Model-free control design data-driven control Optimal control

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Learning to Reach, Swim, Walk and Fly in One Trial: data-driven control with Scarce data and Side Information 4

Learning to Reach, Swim, Walk and Fly in One Trial: Data-Dri...

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4th Annual Conference on Learning for Dynamics and control (L4DC)

作者： Djeumou, Franck Topcu, Ufuk Univ Texas Austin Austin TX 78712 USA

We develop a learning-based control algorithm for unknown dynamical systems under very severe data limitations. Specifically, the algorithm has access to streaming and noisy data only from a single and ongoing trial. It accomplishes such performance by effectively leveraging various forms of side information on the dynamics to reduce the sample complexity. Such side information typically comes from elementary laws of physics and qualitative properties of the system. More precisely, the algorithm approximately solves an optimal control problem encoding the system's desired behavior. To this end, it constructs and iteratively refines a data-driven differential inclusion that contains the unknown vector field of the dynamics. The differential inclusion, used in an interval Taylor-based method, enables to over-approximate the set of states the system may reach. Theoretically, we establish a bound on the suboptimality of the approximate solution with respect to the optimal control with known dynamics. We show that the longer the trial or the more side information is available, the tighter the bound. Empirically, experiments in a high-fidelity F-16 aircraft simulator and MuJoCo's environments illustrate that, despite the scarcity of data, the algorithm can provide performance comparable to reinforcement learning algorithms trained over millions of environment interactions. Besides, we show that the algorithm outperforms existing techniques combining system identification and model predictive control.

关键词： Physics-informed learning data-driven control system identification reachable sets

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A bias-variance perspective of data-driven control

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IFAC-PapersOnLine 2024年第15期58卷 85-90页

作者： Kévin Colin Yue Ju Xavier Bombois Cristian R. Rojas Håkan Hjalmarsson Centre for Advanced Bio Production AdBIOPRO KTH Royal Institute of Technology Sweden Division of Decision and Control Systems KTH Royal Institute of Technology Sweden Laboratoire Ampère UMR CNRS 5005 Ecole Centrale de Lyon Université de Lyon France Centre National de la Recherche Scientifque (CNRS) France

data-driven control, the task of designing a controller based on process data, finds application in a wide range of disciplines and the topic has been intensively studied over more than half a century. The main purpose of this contribution is to elucidate on the commonalities between data-driven control and parameter estimation. In particular, we discuss the bias-variance trade-of, i.e. rather than aiming for the optimal controller one should aim for a constrained version, that may be characterized by tunable parameters, corresponding to hyperparameters in parameter estimation. As a result we shift attention from indirect vs direct data driven control by highlighting the important role played by (complete) minimal sufficient statistics. To keep technicalities at a minimum, still capturing the essential features of the problem, we consider the problem of minimizing the expected control cost for a quadratic open loop control problem applied to a finite impulse response system. In a Gaussian white noise setting, the maximum-likelihood parameter estimate constitutes a complete minimal sufficient statistic which allows us to focus on controllers that are functions of this model estimate without loss of statistical accuracy. We make a systematic study of three different controller structures and two different tuning techniques and illustrate their behaviours numerically.

关键词： data-driven control Bayes control Regularization Kernel Methods

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A data-driven control to throttle valves

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control ENGINEERING PRACTICE 2023年第1期139卷

作者： Witrant, Emmanuel Landau, Ioan Dore Vaillant, Marie-Pierre Univ Grenoble Alpes GIPSA Lab CNRS F-38000 Grenoble France Univ Grenoble Alpes CNRS G2Elab F-38000 Grenoble France

Electric throttle valves represent a challenge for control design, as their dynamics involves strong nonlinearities, characterized by an asymmetric hysteresis. Large variability in the characteristics of each valve and erratic steady-state behaviors can also be noticed by carrying out experiments on multiple valves, impairing classical model-based control strategies. Nevertheless, local data-driven linear models can be obtained by system identification, and simple proportional-integral (PI) digital controllers can be tuned individually for each valve, providing good tracking performance. As these controllers cannot be transposed from one valve to another, a robust control design is considered. Taking into account the variability of electric throttle valves, a real-time data-driven strategy is then proposed, using identification in closed-loop and controller re-design. This methodology is necessary if control performance is a key issue, and can be embedded on a low-cost controller board (Arduino & REG;Mega 2560). Experimental results going from frequency analysis and linear design to real-time data-driven control illustrate the methodology presented in the paper.

关键词： data-driven control System identification PI design Adaptive control Electronic throttle control (ETC)

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data-driven control of LVDC Network Converters: Active Load Stabilization

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IEEE TRANSACTIONS ON SMART GRID 2020年第3期11卷 2182-2194页

作者： Ruiz-Martinez, Omar F. Mayo-Maldonado, Jonathan C. Escobar, Gerardo Frias-Araya, Benjamin A. Valdez-Resendiz, Jesus E. Rosas-Caro, Julio C. Rapisarda, Paolo Tecnol Monterrey Sch Engn & Sci Tecnol Monterrey 64849 Mexico Univ Panamer Acad Elect Elect & Control Zapopan 45010 Mexico Univ Southampton Vis Learning & Control Grp Southampton SO IBJ Hants England

This paper addresses the (model-free) data-driven control of power converters, acting as distributed generators, in low voltage direct current (LVDC) networks (e.g., DC microgrids, DC distributed power systems, DC buses wit multiple sources and loads, etc.). Since traditional stand-alone control design, cannot guarantee stability when converters are connected to a network, it is proposed a deterministic solution that does not require the network model-an approach purely based on measurement data. This is a suitable way to overcome common issues when using a model-based approach, e.g., the use of an excessive number of variables and equations, the presence of un-modeled dynamics, unknown parameters and/or the lack of first principle model equations. To corroborate the advantages of the proposed approach, the present work addresses an extreme but also realistic scenario: weak networks with active loads, such as constant power loads (CPLs). It is also shown that the proposed scheme guarantees stability in a rigorous deterministic way-using a Lyapunov approach based on coefficient matrices directly constructed from data. Simulation results using a multi-bus LVDC distribution network, based on PSCAD/EMTDC, are provided as proof of concept.

关键词： Mathematical model Power system stability Load modeling Stability analysis control design Performance evaluation Generators data-driven control distributed generators LVDC networks DC microgrids DC buses oscillations stability constant power loads

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data-driven control of micro-climate in buildings: An event-triggered reinforcement learning approach

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APPLIED ENERGY 2020年 277卷 115451-115451页

作者： Hosseinloo, Ashkan Haji Ryzhov, Alexander Bischi, Aldo Ouerdane, Henni Turitsyn, Konstantin Dahleh, Munther A. MIT Lab Informat & Decis Syst Cambridge MA 02139 USA DE Shaw Grp New York NY 10036 USA Skolkovo Inst Sci & Technol Ctr Energy Sci & Technol 3 Nobel St Skolkovo 121205 Moscow Region Russia

Smart buildings have great potential for shaping an energy-efficient, sustainable, and more economic future for our planet as buildings account for approximately 40% of the global energy consumption. Future of the smart buildings lies in using sensory data for adaptive decision making and control that is currently gloomed by the key challenge of learning a good control policy in a short period of time in an online and continuing fashion. To tackle this challenge, an event-triggered - as opposed to classic time-triggered - paradigm, is proposed in which learning and control decisions are made when events occur and enough information is collected. Events are characterized by certain design conditions and they occur when the conditions are met, for instance, when a certain state threshold is reached. By systematically adjusting the time of learning and control decisions, the proposed framework can potentially reduce the variance in learning, and consequently, improve the control process. We formulate the micro-climate control problem based on semi-Markov decision processes that allow for variable-time state transitions and decision making. Using extended policy gradient theorems and temporal difference methods in a reinforcement learning set-up, we propose two learning algorithms for event-triggered control of micro-climate in buildings. We show the efficacy of our proposed approach via designing a smart learning thermostat that simultaneously optimizes energy consumption and occupants' comfort in a test building.

关键词： Event-triggered learning Smart buildings Reinforcement learning data-driven control Energy efficiency Cyber-physical systems

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Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey

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ANNUAL REVIEWS IN control 2023年 56卷

作者： Martin, Tim Schoen, Thomas B. Allgoewer, Frank Univ Stuttgart Inst Syst Theory & Automatic Control Stuttgart Germany Uppsala Univ Dept Informat Technol Uppsala Sweden

This survey presents recent research on determining control-theoretic properties and designing controllers with rigorous guarantees using semidefinite programming and for nonlinear systems for which no mathematical models but measured trajectories are available. data-driven control techniques have been developed to circumvent a time-consuming modelling by first principles and because of the increasing availability of data. Recently, this research field has gained increased attention by the application of Willems' fundamental lemma, which provides a fertile ground for the development of data-driven control schemes with guarantees for linear time-invariant systems. While the fundamental lemma can be generalized to further system classes, there does not exist a comparable data-based system representation for nonlinear systems. At the same time, nonlinear systems constitute the majority of practical systems. Moreover, they include additional challenges such as data-based surrogate models that prevent system analysis and controller design by convex optimization. Therefore, a variety of data-driven control approaches has been developed with different required prior insights into the system to ensure a guaranteed inference. In this survey, we will discuss developments in the context of data-driven control for nonlinear systems. In particular, we will focus on methods based on system representations providing guarantees from finite data, while the analysis and the controller design boil down to convex optimization problems given as semidefinite programming. Thus, these approaches achieve reasonable advances compared to the state-of-the-art system analysis and controller design by models from system identification. Specifically, the paper covers system representations based on extensions of Willems' fundamental lemma, set membership, kernel techniques, the Koopman operator, and feedback linearization.

关键词： data-driven control data-driven system analysis Nonlinear systems Semidefinite programming

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