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data-driven control of Distributed Event-Triggered Network Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 351-364页

作者： Xin Wang Jian Sun Gang Wang Frank Allgower Jie Chen the National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the Institute for Systems Theory and Automatic control University of StuttgartStuttgart 70550Germany the Department of control Science and Engineering Tongji UniversityShanghai 201804 the National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(*** systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional *** the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is ***,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input ***,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.

关键词： data-driven control distributed event-triggered network system(ETS) linear matrix inequalitie(LMI) looped-functional stability

A Novel Robust Formulation of Direct data-driven Predictive control

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A Novel Robust Formulation of Direct Data-Driven Predictive ...

第35届中国过程控制会议

作者： Yibo Wang Ling Wang Dexian Huang Chao Shang Department of Automation Tsinghua University

Recently, the data-driven predictive control(DDPC) methods have received a growing interest due to their capability to directly obtain control policy from raw data without identifying a parametric model. However, in the presence of noise corruption and disturbance, output predictions in DDPC become less reliable, which may yield unexpected control actions. In this paper,we put forward a novel robust DDPC method by considering uncertainty in the unidentifiable subspace. We first uncover the lack of robustness of previous remedies of DDPC via a min-min viewpoint. Based on this, a min-max optimization problem is proposed, where uncertainty is addressed from a pessimistic perspective to ensure the robustness. A computationally tractable reformulation of the min-max problem is developed. Furthermore, in order to reduce the computational complexity, we propose an efficient preconditioning method. Finally, numerical examples showcase the improved control performance of the proposed robust method over the known regularized formulations.

关键词： data-driven control Fundamental Lemma Predictive control Robust optimization

Parametric data-driven control Method of Hybrid System via Virtual Reference Feedback Tuning

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Parametric Data-driven Control Method of Hybrid System via V...

第36届中国控制会议

作者： Liu Jiaqi Li Shuangquan Lyu Ning Gao Xiaozhi School of Automation Harbin University of Science and Technology Department of Electrical Engineering and Automation Aalto University

ISBN: (纸本)9781538629185

This paper presents a data-driven method to design parameterized controller for a class of hybrid *** method relies on the virtual reference feedback tuning（VRFT） that is ongoing does not derive any detailed model knowledge of hybrid *** other model-based control methods,the VRFT method direct use of the measurement I/O data and select desired controllers by employing optimization tools from virtual reference ***,the optimal variables are directly as the control parameters of the hybrid *** improve the efficiency of control parameter optimization,a particle swarm optimization（PSO） algorithm with a ε-dominance strategy is used to implement the developed *** addition,the effectiveness of the method is demonstrated experimentally on a leader-following tracking task of multiple unmanned aerial vehicles（multi-UAVs） formation flight.

关键词： hybrid system data-driven control global model reference optimization parameterized controller design

data-driven control of Autonomous Vehicle using Recurrent Fuzzy Neural Network Combined with PID Method

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Data-driven Control of Autonomous Vehicle using Recurrent Fu...

第37届中国控制会议

作者： Yu Han Qidan Zhu Yao Xiao College of Automation Harbin Engineering University

This paper proposes a data-driven control method of autonomous vehicle to deal with target-reaching and obstacle avoidance *** unknown environment and modeling error bring uncertainties to control system,performance of accurate controller is *** needs to be designed online according to online and off-line *** network possesses good nonlinear approximation and learning ***,a kind of recurrent fuzzy neural network is used and related learning method is designed to predict optimal control *** improve robustness of control system,online tuning PID method is designed to obtain control *** establishing constrained optimization strategy,suitable parameters of PID are got to guarantee optimization of control under *** results prove that controller proposed in this paper is effective.

关键词： Autonomous Vehicle Recurrent Fuzzy Neural Network PID Robust Optimal control data-driven control

data-driven consensus control of fully distributed event-triggered multi-agent systems

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Science China(Information Sciences) 2023年第5期66卷 147-161页

作者： Yifei LI Xin WANG Jian SUN Gang WANG Jie CHEN School of Automation Beijing Institute of Technology The State Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Department of control Science and Engineering Tongji University

This study investigates the consensus control issue in discrete-time linear multi-agent systems(MASs) using data-driven control under undirected communication networks. To alleviate the communication burden, an adaptive event-triggered control strategy involving only local information is proposed and a model-based stability condition is derived that guarantees the asymptotic consensus of MASs. Furthermore,a data-based consensus condition for unknown MASs is established by combining a data-based system representation with the model-based stability condition, using only pre-collected noisy input-state data instead of the accurate system information a priori. Specifically, both model-based and data-driven event-triggered controllers can be utilized without requiring any global information. The validity and correctness of the controllers and associated theoretical results are demonstrated via numerical simulations.

关键词： distributed control event-triggered control data-driven control discrete-time MASs

An On-line data-driven control Method for Nonlinear System on Deterministic Interval Hull Scheme

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An On-line Data-driven Control Method for Nonlinear System o...

第三十九届中国控制会议

作者： Li Yuhang Cao He Liu Jiaqi School of Automation Harbin University of Science and Technology

This paper presents a data-driven control method for trajectory tracking of nonlinear system by operating input-output measurement *** method directs the use of the measurement data where the state estimation set is approximated by its interval hull,which is ongoing by considering unknown but bounded perturbations and measurement *** deterministic interval hull is provided to select robust constraint of desired control stability by employing optimization tools of kernel function approximation,and predictable control behavior is guaranteed by making use of a batch of available *** improve the tracking performance over time,a guaranteed accuracy bound is derived to quantify the tracking error bound between the optimal controller and the approximate controller under the on-line data-driven method by alternate using measurement step and correction *** method is generally applied to set-membership state estimation *** particular,the set can have different geometric ***,the main theoretical properties of the method are demonstrated experimentally on a sensor transmission power control for state estimation,and the result demonstrates the expected state estimation error covariance.

关键词： nonlinear trajectory tracking data-driven control interval hull kernel approximation

Distributed data-driven consensus control of multi-agent systems under switched uncertainties

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control Theory and Technology 2023年第3期21卷 478-487页

作者： Wenjie Liu Yifei Li Gang Wang Jian Sun Jie Chen National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China Department of control Science and Engineering Tongji UniversityShanghai 201804China

Cooperative control of multi-agent systems(MASs),particularly consensus control,has gained significant attention in the last two decades,thanks to the rapid and sustained development of distributed and networked *** this paper,we present some new results focused on consensus control of a set of unknown linear MASs(whose system matrices are unknown)under unknown switched uncertainties,with an emphasis on distributed data-driven *** proposed controller is end-to-end,designed by solving two data-based semi-definite programs(SDPs),which adjust to the changes of the uncertainty *** approach achieves asymptotic consensus of the MAS provided that the switching is slow enough and the uncertainty is *** illustrate the effectiveness of our proposed method through a numerical example.

关键词： data-driven control Multi-agent systems Consensus-Switched uncertainties

State Estimation of Hybrid data-driven control System via Zonotopic Bounding Set Computation

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State Estimation of Hybrid Data-driven Control System via Zo...

第三十九届中国控制会议

作者： Zhang Shaotong Li Yuhang Liu Jiaqi School of Automation Harbin University of Science and Technology

This paper presents a data-driven method to obtain state estimation for a class of hybrid control systems,which involves interacting discrete-event and continuous-variable *** the system with no well-established model has made parameter optimization of the controller cannot be *** method relies on the virtual reference feedback tuning（VRFT）that ongoing direct use of the measurement I/O data and select desired controllers by employing output-error parameter estimation from virtual reference *** improve the efficiency to compute a feasible state set that contains the parameters consistent with the measured data and the given noise and perturbation,a set membership estimation by zonotopes is used to implement the developed ***,the effectiveness of the method is demonstrated experimentally on a leader-following tracking task of multiple unmanned aerial vehicles（multi-UAVs） formation flight.

关键词： hybrid system data-driven control virtual reference feedback tuning zonotopic set membership estimation

Machine Learning and data-driven Techniques for the control of Smart Power Generation Systems:An Uncertainty Handling Perspective

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Engineering 2021年第9期7卷 1239-1247页

作者： Li Sun Fengqi You Key Laboratory of Energy Thermal Conversion and control of Ministry of Education School of Energy and the EnvironmentSoutheast UniversityNanjing 210096China Robert Frederick Smith School of Chemical and Biomolecular Engineering Cornell UniversityIthacaNY 14853USA

Due to growing concerns regarding climate change and environmental protection,smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable *** first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various *** burgeoning era of machine learning(ML)and data-driven control(DDC)techniques promises an improved alternative to these outdated *** paper reviews typical applications of ML and DDC at the level of monitoring,control,optimization,and fault detection of power generation systems,with a particular focus on uncovering how these methods can function in evaluating,counteracting,or withstanding the effects of the associated uncertainties.A holistic view is provided on the control techniques of smart power generation,from the regulation level to the planning *** benefits of ML and DDC techniques are accordingly interpreted in terms of visibility,maneuverability,flexibility,profitability,and safety(abbreviated as the“5-TYs”),***,an outlook on future research and applications is presented.

关键词： Smart power generation Machine learning data-driven control Systems engineering