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Online Stochastic Optimization for Unknown Linear systems: data-driven controller Synthesis and Analysis

ieee TRANSACTIONS ON AUTOMATIC control 2024年第7期69卷 4411-4426页

作者： Bianchin, Gianluca Vaquero, Miguel Cortes, Jorge Dall'Anese, Emiliano Univ Louvain ICTEAM Inst B-1348 Ottignies Louvain La Neuv Belgium Univ Louvain Dept Math Engn B-1348 Ottignies Louvain La Neuv Belgium IE Univ Sch Sci & Technol Segovia 40003 Spain Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA Univ Colorado Boulder Dept Elect Comp Energy Engn Boulder CO 80309 USA

This article proposes a data-driven control framework to regulate an unknown stochastic linear dynamical system to the solution of a stochastic convex optimization problem. Despite the centrality of this problem, most of the available methods critically rely on a precise knowledge of the system dynamics, thus requiring offline system identification. To solve the control problem, we first show that the steady-state gain of the transfer function of a linear system can be computed directly from historical data generated by the open-loop system, thus overcoming the need to first identify the full system dynamics. We leverage this data-driven representation of the steady-state gain to design a controller, which is inspired by stochastic gradient descent methods, to regulate the system to the solution of the prescribed optimization problem. A distinguishing feature of our method is that it does not require any knowledge of the system dynamics or of the possibly time-varying disturbances affecting them (or their distributions). Our technical analysis combines concepts from behavioral system theory, stochastic optimization with decision-dependent distributions, and Lyapunov stability. We illustrate the applicability of the framework in a case study for mobility-on-demand ride service scheduling in Manhattan.

关键词： Optimization Stochastic processes control systems Trajectory Steady-state Power system dynamics Linear systems control design data-driven control learning systems optimization methods stochastic optimization shared transport

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Error tracking learning control for nonlinearly parameterized systems with input deadzone 12

Error tracking learning control for nonlinearly parameterize...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Fan, Longwei Yan, Qiuzhen Chen, Qiang Zhejiang Univ Water Resources & Elect Power Coll Informat Engn Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China

ISBN: (纸本)9798350321050

The trajectory tracking problem for nonlinearly parameterized uncertain systems with unknown input deadzone is investigated in this work. Lyapunov-based ILC strategy is adopted for controller design. Unlike traditional ILC methods, in this paper, the initial condition for each iteration allows to be any bounded value. By using the parameter separation technique combined with the signal replacement mechanism, a Lyapunov functional is constructed to design the ILC law and learning laws. As the iteration cycle increases, the system error can follow the predetermined desired trajectory over the entire interval, and all the signals in close-loop system are guaranteed to be bounded. Effectiveness of the proposed method is verified by theoretical results and numerical results.

关键词： Adaptive iterative learning control initial position problem nonlinearly parameterized systems

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Inverse Reinforcement learning for Discrete-Time Linear Quadratic systems 14

Inverse Reinforcement Learning for Discrete-Time Linear Quad...

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14th Asian control conference (ASCC)

作者： Yu, Meiling Ni, Yuanhua Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China

ISBN: (纸本)9798331540845;9789887581598

In this article, we focus on the discrete-time stochastic linear quadratic problem under the presence of process and observation noise, particularly within the framework of average cost setting, exploring the optimal policy based on output feedback mechanisms. This paper introduces a data-driven inverse reinforcement learning algorithm designed to reconstruct an unknown cost function and learn a near-optimal control policy solely based on observed optimal behavior trajectories (input-output pairs) in scenarios where the cost function is unknown. Initially, we present a model-based inverse reinforcement learning approach under the premise of known model parameters, followed by a proof of theoretical equivalence between this method and our proposed data-driven approach. This equivalence not only validates the theoretical soundness of the proposed data-driven method but also ensures the convergence of the algorithm through theoretical analysis. Ultimately, through carefully designed numerical simulation experiments, we demonstrate the effectiveness of the proposed algorithm, confirming its ability to successfully reconstruct the cost function and learn an effective policy based on demonstration trajectories under unknown cost function conditions.

关键词： inverse reinforcement learning linear quadratic problem data-driven control output feedback control

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Siamese Convolutional Neural Network Based Visual Servo for Manipulator 12

Siamese Convolutional Neural Network Based Visual Servo for ...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Deng, Gaofeng Liu, Shan Zhejiang Univ State Key Lab Ind Control Technol Coll Control Sci & Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9798350321050

A visual servo algorithm based on Siamese Convolution Neural Network is proposed for the manipulator to avoid the requirement of feature extraction and feature matching in the traditional image-based visual servo (IBVS). The algorithm feeds the current image and the desired image into the network at the same time, and outputs the relative pose difference between the two images. A closed-loop control system is constructed through the pose difference, and control the end-effector of the manipulator to reach the desired position to grasp the target workpiece. Meanwhile, in order to meet the large amount of data needed in training the neural network, an algorithm to automatically generate the data set is proposed, which can avoid manual collection and labeling of the data set and greatly save the cost. The simulations show the effectiveness and accuracy of the proposed method by comparing with the traditional feature point based IBVS, and the grasping experiment shows the feasibility of the proposed method in actual practice.

关键词： Visual Servo Manipulator Pose Estimation Siamese Convolutional Neural Network Composite data

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Iterative learning control for 2-D Discrete systems in Frequency Domain 12

Iterative Learning Control for 2-D Discrete Systems in Frequ...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Wan, Kai Xu, Qing-yuan Huizhou Univ Sch Elect Informat & Elect Engn Huizhou 516007 Peoples R China Guangdong Polytech Normal Univ Sch Elect & Informat Guangzhou 510665 Peoples R China

ISBN: (纸本)9798350321050

In this paper, the robust convergence problem of iterative learning control (ILC) is investigated for two-dimensional (2-D) discrete systems with iteration-varying boundary states and errors in the frequency domain. A classical P-type ILC law is designed. By using 2-D Z-transform analysis, a sufficient condition of the ILC law can be obtained. By the rigorous mathematical proof, the ultimate ILC tracking error can converge to a bounded region, which is dependent on the upper bound of boundary states/errors. In particular, when all the boundary states and errors are zero, the practical tracking output can precisely track a 2-D desired trajectory.

关键词： Iterative learning control (ILC) frequency domain 2-D Z-transform

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Gain-varying P-type ILC for Nonlinear Multi-agent systems against FDI Attacks 13

Gain-varying P-type ILC for Nonlinear Multi-agent Systems ag...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Peng, Huiming Lin, Na Chi, Ronghu School of Automation & Electronic Engineering Qingdao University of Science & Technology Qingdao266061 China

ISBN: (纸本)9798350361674

This work studies the consensus learning control problem of nonlinear multi-agent systems (MASs) under false data injection (FDI) attacks. Applying the attacked output and consensus error information to the control law and the parameter updating algorithm effectively reduces the impact of FDI attacks. The provided control scheme, which is entirely data-driven and more suited for real-world industrial applications, makes no use of any model knowledge from the controlled MASs. In addition, compared with the P-type ILC method, the variable gain effectively improves the robustness of the presented GV-PILC scheme to the external uncertainties. The simulation results verify the effectiveness of the proposed method by comparing with fixed gain P-type ILC. © 2024 ieee.

关键词： Multi agent systems

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Cascaded Generalized Extended State Observer-Based control for Servo systems With Matched and Mismatched Disturbances 12

Cascaded Generalized Extended State Observer-Based Control f...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Jiang, Fuxi Ye, Jie Cheng, Shanmei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9798350321050

This paper proposes a cascaded generalized extended state observer-based control (CGESOBC) implementation scheme for a class of nonlinear servo systems with nonintegral-chain form and multiple matched and mismatched disturbances. In this approach, the total disturbances in each channel are reconstructed by designing a GESO. A reference model is developed with the estimated disturbances and the reference input, together with a state tracking error model containing the multiple residual disturbances. Another GESO is then devised to estimate the primary estimation errors, based on which a state feedback control law incorporating a dynamic compensator is formulated for robust stabilization of the state tracking error system. Moreover, the Lyapunov stability theory is applied to prove the bounded stability of the closed-loop system. Finally, the efficacy of the proposed control method is verified by a numerical example.

关键词： Nonlinear servo system Mismatched disturbance Generalized extended state observer Robust stabilization

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Optimized SAC Deep Reinforcement learning control for Electro-hydraulic Servo systems 12

Optimized SAC Deep Reinforcement Learning Control for Electr...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Yu, Zhen Hu, Huiteng Jiang, Mengman Tang, Yifan Qian, Rongrong Liu, Lijun Xiamen Univ Sch Aerosp Engn Xiamen Peoples R China Xiamen Univ Shenzhen Res Inst Shenzhen Peoples R China AECC Commercial Aircraft Engine Co LTD Dept Adv Technol Shanghai Peoples R China

ISBN: (纸本)9798350321050

In this paper, a reinforcement learning environment based on a polynomial nonlinear model of an electro-hydraulic servo system is established, and an optimized state space sparse reward function is designed to improve the exploration ability of the SAC algorithm under sparse rewards using random network distillation (RND). The control performance of the designed optimized SAC deep reinforcement learning controller is verified through the semi-physical simulation experiment platform, and the time-varying signal is designed according to different tasks to test the dynamic control performance of the controller under complex tasks. The experimental results prove that the optimized SAC deep reinforcement learning controller proposed in this paper has good control performance and strong robustness.

关键词： SAC algorithm random network distillation electro-hydraulic servo system

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Reinforcement learning for Synchronization of Heterogeneous Multiagent systems by Improved Q-Functions

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ieee TRANSACTIONS ON CYBERNETICS 2024年第11期54卷 6545-6558页

作者： Li, Jinna Yuan, Lin Cheng, Weiran Chai, Tianyou Lewis, Frank L. Liaoning Petrochem Univ Sch Informat & Control Engn Fushun 113001 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Univ Texas Arlington UTA Res Inst Arlington TX 76118 USA

This article dedicates to investigating a methodology for enhancing adaptability to environmental changes of reinforcement learning (RL) techniques with data efficiency, by which a joint control protocol is learned using only data for multiagent systems (MASs). Thus, all followers are able to synchronize themselves with the leader and minimize their individual performance. To this end, an optimal synchronization problem of heterogeneous MASs is first formulated, and then an arbitration RL mechanism is developed for well addressing key challenges faced by the current RL techniques, that is, insufficient data and environmental changes. In the developed mechanism, an improved Q-function with an arbitration factor is designed for accommodating the fact that control protocols tend to be made by historic experiences and instinctive decision-making, such that the degree of control over agents' behaviors can be adaptively allocated by on-policy and off-policy RL techniques for the optimal multiagent synchronization problem. Finally, an arbitration RL algorithm with critic-only neural networks is proposed, and theoretical analysis and proofs of synchronization and performance optimality are provided. Simulation results verify the effectiveness of the proposed method.

关键词： Synchronization Protocols Heuristic algorithms Decision making Nash equilibrium Multi-agent systems Games data-driven control distributed control multiagent systems (MASs) reinforcement learning (RL) synchronization

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Iterative learning Fault Estimation for Stochastic Time-varying systems 13

Iterative Learning Fault Estimation for Stochastic Time-vary...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Tang, Jiawen Zhang, Wenli Liu, Anning Shi, Jiantao Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China

ISBN: (纸本)9798350361674

The article investigates the fault estimation problem for a class of stochastic time-varying systems based on iterative learning methods. Unlike most traditional works, the system investigated in this paper is affected by disturbances, noises and initial state deviations. A type of iterative learning fault estimator is designed utilizing the historical output estimation errors for receiving the fault information of the system. Furthermore, the existence condition is analyzed in order to meet boundedness with respect to the A-norm of fault estimation errors based on the proposed iterative learning scheme. At last, a case study is applied for verify the effectiveness of the studied scheme. © 2024 ieee.

关键词： Contrastive learning

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