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data-driven control for Continuous-Time Delayed Systems Under Denial-of-Service Attacks and Unknown Measurement Noise

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第6期55卷 3814-3823页

作者： Zou, Yi Tian, Engang Chen, Huwei Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Jiangsu Aerosp Power Machinery & Elect Co Ltd Technol Dev Ctr Taizhou 225300 Peoples R China

This article proposes a novel data-driven approach for studying the stabilization of continuous-time delayed systems (CDSs) with both unknown model parameters and denial-of-service (DoS) attacks. Due to the effects of DoS attacks, such controlled CDSs can be viewed as a switched system with two modes: a stable mode during dormant intervals and an unstable one during attack intervals. Because of the switching character of the CDS with DoS attacks, some well-known data-driven control methods can not be used directly. To tackle this issue, a new relationship between the dormancy and activation periods of DoS attacks is built, which offers a more reasonable description of DoS attacks. Additionally, in the presence of measurement noise, a novel lemma (Lemma 3) is introduced to establish a data-based representation of CDSs using noisy measurement data. Without the need of explicit system parameter identification, stabilization criteria and control gain matrix are derived solely based on the input-state data. Finally, the effectiveness and good performance of the proposed method are illustrated through a numerical example and a power system.

关键词： Noise measurement Denial-of-service attack control systems Symmetric matrices Noise Switched systems Delay effects Asymptotic stability Vectors Power system stability Continuous-time system data-driven control Denial-of-Service (DoS) attacks noisy data time delay

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data-driven control of Linear Systems via Quantized Feedback

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Journal of Systems Science & Complexity 2024年第1期37卷 152-168页

作者： LI Xingchen ZHAO Feiran YOU Keyou Department of Automation and BNRist Tsinghua UniversityBeijing 100084China

Quantized feedback control is fundamental to system synthesis with limited communication *** sharp contrast to the existing literature on quantized control which requires an explicit dynamical model,the authors study the quadratic stabilization and performance control problems with logarithmically quantized feedback in a direct data-driven framework,where the system state matrix is not exactly known and instead,belongs to an ambiguity set that is directly constructed from a finite number of noisy system *** this end,the authors firstly establish sufficient and necessary conditions via linear matrix inequalities for the existence of a common quantized controller that achieves our control objectives over the ambiguity ***,the authors provide necessary conditions on the data for the solvability of the LMIs,and determine the coarsest quantization density via semi-definite *** theoretical results are validated through numerical examples.

关键词： data-driven control linear matrix inequalities linear systems quantized control.

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data-driven control of Linear Parabolic Systems Using Koopman Eigenstructure Assignment

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IEEE TRANSACTIONS ON AUTOMATIC control 2025年第1期70卷 665-672页

作者： Deutscher, Joachim Ulm Univ Inst Measurement Control & Microtechnol D-89081 Ulm Germany

This article considers the data-driven stabilization of linear boundary controlled parabolic PDEs by making use of the Koopman operator. For this, a Koopman eigenstructure assignment problem is solved, which amounts to determining a feedback of the Koopman open-loop eigenfunctionals assigning a desired finite set of closed-loop Koopman eigenvalues and eigenfunctionals to the closed-loop system. It is shown that the designed controller only needs a finite number of open-loop Koopman eigenvalues and modes of the state. They are determined by extending the classical Krylov-dynamic mode decomposition (DMD) to parabolic systems. For this, only a finite number of pointlike outputs and their temporal samples, as well as temporal samples of the inputs, are required, resulting in a data-driven solution to the eigenstructure assignment problem. Exponential stability of the closed-loop system in the presence of small Krylov-DMD errors is verified. An unstable diffusion-reaction system demonstrates the new data-driven controller design technique for distributed-parameter systems.

关键词： Eigenvalues and eigenfunctions Generators Closed loop systems State feedback Aerospace electronics Systematics Spectral analysis data-driven control Koopman operator modal approach parabolic systems state feedback

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data-driven control of COVID-19 in Buildings: A Reinforcement-Learning Approach

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第4期21卷 5691-5699页

作者： Hosseinloo, Ashkan Haji Nabi, Saleh Hosoi, Anette Dahleh, Munther A. MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Mitsubishi Elect Res Labs Cambridge MA 02139 USA MIT Dept Mech Engn Cambridge MA 02139 USA

In addition to its public health crisis, COVID-19 pandemic has led to the shutdown and closure of workplaces with an estimated total cost of more than $16 trillion. Given the long hours an average person spends in buildings and indoor environments, this research article proposes data-driven control strategies to design optimal indoor airflow to minimize the exposure of occupants to viral pathogens in built environments. A general control framework is put forward for designing an optimal velocity field and proximal policy optimization, a reinforcement learning algorithm is employed to solve the control problem in a data-driven fashion. The same framework is used for optimal placement of disinfectants to neutralize the viral pathogens as an alternative to the airflow design when the latter is practically infeasible or hard to implement. We show, via computational simulations, that the control agent learns the optimal policy in both scenarios within a reasonable time. The proposed data-driven control framework in this study will have significant societal and economic benefits by setting the foundation for an improved methodology in designing case-specific infection control guidelines that can be realized by affordable ventilation devices and *** to Practitioners-This paper is motivated by the problem of COVID-19 infection spread in enclosed spaces but it also applies to other airborne pathogens. Airborne disease contagion often takes place in indoor environments;however, ventilation systems are almost never designed to take this into account so as to contain the spread of the pathogens. This is mainly because airflow design requires solving high-dimensional nonlinear partial differential equations known as Navier Stokes equations in fluid dynamics. In this paper, we propose a data-driven approach for solving the control problem of pathogen containment without solving the fluid dynamics equations. To this end, we first mathematically formulate the problem

关键词： Disease control reinforcement learning data-driven control HVAC system

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data-driven control for Nonlinear Networked Systems on Basis of Two Description Coding Scheme

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第4期21卷 6721-6730页

作者： Zhang, Shuhua Ma, Lifeng Yi, Xiaojian Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing 314000 Peoples R China

This paper deals with the tracking control problem for a type of nonlinear networked systems by utilizing the data-driven control algorithm. A scalar-uniform-quantization-based two description coding mechanism is employed, with the hope to alleviate communication bandwidth limitation and packet dropouts phenomenon during data transmission. Such a mechanism first encodes the source signal into two descriptions, and then send the coded information to the decoder via independent network channels. The random packet dropouts phenomenon is considered that is described via Bernoulli sequences. A data-driven control scheme is designed, ensuring that the system output can track the desired reference. By resorting to the dynamic linearization method in combination with certain convex optimization technique, the desired control protocol is established. Finally, the usefulness of our designed control approach is demonstrated via two examples. Note to Practitioners-Networked systems are composed of sensors, actuators and controllers connected through a shared digital communication network, which have advantages in long-distance control operations. It should be noted that, however, during network communication, data packets would probably suffer dropout due to various reasons such as limited bandwidth, environment abrupt change, device aging and so on, which will degrade system performance or even damage the system. However, so far, in the context of data-driven control, where data play an essential role, such a data-lost phenomenon has not been fully examined when designing the control strategies. Therefore, the main motivation of this paper is to study the tracking control problem of networked systems with data packet dropouts. In this work, we provide an efficient control algorithm that enables the system to track the desired target in spite of packet loss. The core of this method is to transmit data through two independent network channels so as to reduce the probability of t

关键词： Nonlinear networked systems data-driven control two description coding mechanism limited communication bandwidth packet dropouts

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data-driven control With Prescribed-Time Convergence for Discrete-Time Nonlinear Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 4961-4971页

作者： Li, Yijia Ou, Xianhua Chen, Peng He, Xiongxiong Jiang, Qianru Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China

In this paper, the issue of data-driven prescribed-time convergence control for certain types of discrete-time nonlinear systems is investigated. The dynamic linearization technique and the discrete prescribed-time gain-scheduled feedback function have been combined to create a model-free adaptive prescribed time control strategy. To keep the steady-state error within a bespoke steady-state error band, the controller is switched to a steady-state controller once the system has completed its prescribed time control task. Theoretically, it is demonstrated that the error dynamics can gradually compress from the initial error to the steady-state error band during the initial and prescribed time periods. Furthermore, all of the rule designs in this study are data-driven and solely rely on system input and output data. Manipulator experiments and numerical modeling demonstrate the viability of the proposed strategy. Note to Practitioners-The motivation for this paper stems from the need to design a prescribed-time control method for the industrial robot trajectory tracking control tasks. Prescribed-time control method offers a potential to improve both the speed and coordination of industrial robot trajectory tracking. However, due to the increasing complexity of the robot structure and operating environment, it has brought great difficulties to the use of model-based prescribed-time control. To address this issue, a data-driven prescribed-time control approach is proposed, the controller design and convergence proof through mathematical derivations are also provided. Finally, the effectiveness of the proposed control scheme is validated through numerical simulations and industrial robotic manipulator experiments. This research contributes a viable and efficient time-optimal control method for trajectory-tracking tasks in the realm of industrial robotics.

关键词： control systems Convergence Steady-state Nonlinear systems Adaptation models data models Observers data-driven control prescribed-time convergence model-free adaptive control (MFAC) sliding mode control (SMC)

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data-driven control of Unknown Switched Linear Systems Using Scenario Optimization

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IEEE TRANSACTIONS ON AUTOMATIC control 2024年第11期69卷 7310-7325页

作者： Wang, Zheming Berger, Guillaume O. Jungers, Raphael M. Zhejiang Univ Technol Dept Automat Hangzhou 310032 Peoples R China UCLouvain ICTEAM Inst B-1348 Louvain La Neuve Belgium

We tackle uniform state feedback control of switched linear systems under arbitrary switching using scenario optimization. We propose a data-driven control framework, in which scenario programs are formulated to compute stabilizing state feedback control relying on a finite set of observations of trajectories with quadratic and sum of squares (SOS) Lyapunov functions. We do not require the exact dynamical model or the switching signal, and as a consequence, we aim at solving uniform stabilization problems, in which the feedback is stabilizing for all possible switching sequences. In order to generalize the solution obtained from trajectories to the actual system, probabilistic guarantees on the obtained quadratic or SOS Lyapunov function are derived in the spirit of scenario optimization. For the quadratic Lyapunov technique, the generalization relies on a geometric analysis argument, while, for the SOS Lyapunov technique, we follow a sensitivity analysis argument. In order to deal with high-dimensional systems, we also develop a parallelized scheme for the proposed approach. We show that, with some modifications, the data-driven quadratic Lyapunov technique can be extended to linear quadratic regulator (LQR) control design. Finally, the proposed data-driven control framework is demonstrated on several numerical examples.

关键词： Switches control systems Linear systems State feedback Stability analysis Probabilistic logic Optimization data-driven control scenario optimization stabilization switched linear systems

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data-driven control of Hydraulic Manipulators by Reinforcement Learning

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第4期29卷 2673-2684页

作者： Yao, Zhikai Xu, Fengyu Jiang, Guo-Ping Yao, Jianyong Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China

Motivated by the challenges inherent in achieving high-accuracy tracking control of practical 6-degree-of-freedom (6-DOF) hydraulic robotic manipulators, we aim to conduct research on data-driven control methods in this article. To this end, we introduce actor-critic reinforcement learning to learn the value functions and their corresponding control actions along the trajectories associated with the tracking pattern evolution in hydraulic robotic manipulators. Furthermore, we extend our investigation to encompass the provision of performance guarantees at the system level, even in instances where value functions and control actions are approximated through the utilization of actor-critic neural networks. As proved in this article, the proposed reinforcement learning controller possesses pivotal properties, such as Bellman (sub)optimality in solutions, and the convergence of approximated value functions. We evaluated the proposed reinforcement learning controller in a well-established 6-DOF hydraulic robotic manipulator platform. The experimental results attest to the consistent improvement in control performance, progressively approaching the desired performance benchmarks through iterative updates of control actions. This study makes the first attempt to explore the realization of online adaptive optimal control for hydraulic robotic manipulators within a data-driven paradigm, an area where limited work has been reported, and also sets an example of how reinforcement learning interfaces with industrial automation.

关键词： data-driven control hydraulic systems neural networks reinforcement learning robotic manipulators

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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

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data-driven control Design for Power Converters Approximated as Switched Affine Systems and Experimental Validation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第9期71卷 4236-4240页

作者： Merchan-Riveros, Maria Camila Albea, Carolina Seuret, Alexandre Univ Seville Dept Ingn Sistemas & Automat Seville 41092 Spain

This brief introduces a data-driven control design approach for power converters modelled as a switched affine system, that guarantees the global stability. Unlike many existing approaches, our contribution does not require a precise identification of a nonlinear model but it rather relies on prior random experimental data, which allows the design of a stabilizing control law in a direct manner. The proposed method is then evaluated on an Imperix power test bench. The experimental results not only confirm the viability of the data-driven approach for nonlinear systems but also demonstrate its applicability in real-world scenarios.

关键词： Symmetric matrices Matrix converters Switches control design Uncertainty Vectors Nonlinear systems data-driven control switched affine system power converter

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