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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是31-40 订阅

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Asynchronous data-driven control for Discrete-Time Switched systems with Average Dwell Time 13

Asynchronous Data-Driven Control for Discrete-Time Switched ...

引用

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

作者： Wang, Guandi Chen, Qixin Zhu, Yanzheng College of Mechanical Engineering and Automation Huaqiao University Fujian361021 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266000 China

ISBN: (纸本)9798350361674

the asynchronous data-driven control problem is concerned for discrete-time switched systems with average dwell time. For a class of unknown switched systems, a data-driven approach is employed to parameterize the controller directly from the input-state data of the system, without relying on the precise mathematical model. Furthermore, a data-driven representation of the closed-loop system is proposed to cope with the asynchronous behavior between subsystems and controllers. Stability conditions in the form of linear matrix inequalities are established based on the Lyapunov theory and the average dwell time method, and then an asynchronous data-driven controller design is implemented. Finally, the feasibility and advantage of the proposed approach are verified through numerical examples. © 2024 ieee.

关键词： Discrete time control systems

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Event-Triggered Sampled-data Iterative learning control for Nonlinear Switched Multi-Agent systems 13

Event-Triggered Sampled-Data Iterative Learning Control for ...

引用

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

作者： Ma, Xin Dai, Xisheng Wang, Guoyong School of Automation Guangxi University of Science and Technology Liuzhou545000 China Liuzhou High School Liuzhou545000 China

ISBN: (纸本)9798350361674

this paper investigates a class of consensus tracking problems in switched multi-agent systems(SMASs). For a virtual leader of switched multi-agent systems, preset an expected virtual leader tracking trajectory. Firstly, this paper designs a class of output tracking information that includes adjacent agents and sampled-data iterative learning control. then, this paper designs a sampling-based event-triggered iterative learning control(ETILC) law to control the tracking behavior of agents. Reuse a Lyapunov-like method to determine the event-triggered conditions for system stability. Finally, for nonlinear switched multi-agent systems in various switching subsystems, the convergence conditions of systems can be discussed based on triggering and untriggering in different subsystems, using the Bellman-Gronwall inequality and compression mapping method. Verify the effectiveness and feasibility of the proposed algorithm through an instance of multi-agent systems that includes four switching modes. © 2024 ieee.

关键词： Lyapunov methods

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Adaptive Gain Iterative learning control Based on Extended State Observer for Batch Processes 13

Adaptive Gain Iterative Learning Control Based on Extended S...

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

作者： Li, Dazi Liu, Yanan Xu, Jiahui College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China

ISBN: (纸本)9798350361674

control of batch process encounters challenges in accurate modeling and presents serious nonlinearities. In this paper, an adaptive gain iterative learning control method based on the extended state observer (AG-ILC-ESO) is proposed to enhance the control performance. Addition of an adaptive gain for iterative learning proves effective in correcting the learning direction and enhancing controller performance. this adaptive gain enables real-time adjustments of the control strategy to accommodate system changes. the incorporation of an ESO, based on ILC, facilitates a more precise estimation of system states that are challenging to measure directly. Subsequently, the control system can be adjusted based on the actual system response, leading to gradual improvements in control performance. the proposed method's effectiveness is validated through simulation experiments. © 2024 ieee.

关键词： Batch data processing

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Multivariate Time Series Anomaly Detection with Adaptive Transformer-CNN Architecture Fusing Adversarial Training 13

Multivariate Time Series Anomaly Detection with Adaptive Tra...

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

作者： He, Junpeng Dong, Zhe Huang, Yaqing School of Electrical and Control Engineering North China University of Technology Beijing100144 China

ISBN: (纸本)9798350361674

In the era of IoT, efficient real-time anomaly detection is critical for preventing industrial incidents and ensuring operational integrity. this paper introduces ATC-AFAT (Adaptive Transformer-CNN Architecture Fusing Adversarial Training), a novel unsupervised anomaly detection framework for multivariate time-series data, leveraging an attention-based sequence encoder with adversarial training and CNN discriminate model to tackle the challenges posed by the diverse and nonlinear nature of sensor data. Our method demonstrates significant improvements over existing approaches, reducing training time by 76.5% and enhancing interpretability, as validated on four diverse datasets. ATC-AFAT emerges as a robust solution for real-time anomaly detection in complex IoT environments. © 2024 ieee.

关键词： Adversarial machine learning

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data-driven Fault-Tolerant Distributed Formation control of Multiagent systems with Switching Topologies 13

Data-Driven Fault-Tolerant Distributed Formation Control of ...

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

作者： Zhao, Tianxu Zhang, Xian Wang, Xin Xie, Wen-Bo Heilongjiang University Harbin150080 China

ISBN: (纸本)9798350361674

In this paper, we study the problem of data-driven fault-tolerant formation control for linear multiagent systems with actuator faults in a directed communication switching topology. Initially, constructing a distributed observer to generate using the local information as a reference, the convergence of the observer under the switching topology is proved. Subsequently, we leverage reinforcement learning theory to determine the optimal control strategy, even when the parameters of the subsystems within the multi-agent network are not known. In the final step, we apply the acquired control strategy to devise a data-driven fault-tolerant controller, aimed at minimizing fault impacts as they arise. the efficiency of our proposed solution is confirmed through simulation experiments. © 2024 ieee.

关键词： Reinforcement learning

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Finite-Iteration learning for Multi-Agent systems Using Terminal ILC 13

Finite-Iteration Learning for Multi-Agent Systems Using Term...

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

作者： Chen, Xiaoxue Zhang, Ruikun Lin, Xue School of Mathematics and Physics Qingdao University of Science and Technology Qingdao266061 China

ISBN: (纸本)9798350361674

the article studied a finite-iteration control problem. Based on the multi-agent systems (MASs), where each agent has a linear structure, the article proposed the controller for finite-iteration tracking problem. Combine with the definition of finite-iteration tracking, the main task is to design a control strategy to realize the finite-iteration according to the predetermined error accuracy requirements and other parameter information. To solve the problem, a finite-iteration control strategy is proposed by using terminal iterative learning control (TILC), which contains information of terminal output and control input of the agents. By using the control algorithm, we can realize the terminal output of agents to be consistent after a finite number of iteration. In the end, this article give a numerical simulation example to illustrate the correctness and effectiveness of the proposed finite-iteration control strategy. © 2024 ieee.

关键词： Multi agent systems

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Adaptive learning control for High-Order Uncertain Nonlinear Multiagent systems under Replay Attacks 13

Adaptive Learning Control for High-Order Uncertain Nonlinear...

引用

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

作者： Li, Ying Wang, Youqing Zhao, Dong College of Information Science and Technology Beijing University of Chemical Technology Beijing China School of Cyber Science and Technology Beihang University Beijing China

ISBN: (纸本)9798350361674

In this study, we focus on the adaptive learning control of high-order uncertain nonlinear multiagent systems (MASs) under replay attacks. the neural-network-based adaptive learning scheme is proposed to approximate the nonlinear MASs uncertainty dynamics. Under the undirected graph of MASs communication, the distributed controller and parameter estimator are designed by using the backstepping method. In particular, the boundary condition for the MASs in the presence of replay attacks is analyzed. It is shown that the follower can track the leader asymptotically under replay attacks if the boundary condition is satisfied. Simulation results demonstrate the effectiveness of the proposed theoretical approach. © 2024 ieee.

关键词： Contrastive learning

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Robust data-driven Attitude control for Satellite Using Koopman Operator theory 13

Robust Data-Driven Attitude Control for Satellite Using Koop...

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

作者： Chen, Aijun Bu, Shichao Cui, Lei Zhang, Wenhan Li, Jitao Wang, Zhenhua Shanghai Institute of Satellite Engineering Shanghai201109 China Aerospace System Engineering Shanghai Shanghai201109 China Harbin Engineering University Harbin150001 China Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9798350361674

this paper studies the synthesis of robust control for a satellite attitude control system without pre-knowledge of model dynamics. First, by exploring the time-series data of satellite angular velocity, a lifted discrete-time linear system is constructed using Koopman operator theory and extended dynamic mode decomposition. this system provides an explanation for a finite measurement history. Second, an H∞ controller is designed to attenuate the effect of external disturbances and approximation errors during the stabilization process. the proposed methodology is able to guarantee the robustness performance. Finally, numerical simulations are performed to validate the effectiveness of the proposed method. © 2024 ieee.

关键词： Robust control

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A Novel Hyperbolic-Secant Reaching Law of Discrete-Time Sliding Mode control 13

A Novel Hyperbolic-Secant Reaching Law of Discrete-Time Slid...

引用

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

作者： Zhou, Guoliang Wu, Lingwei School of Intelligent Manufacture Lishui Vocational and Technical College Lishui323000 China School of Intelligent Manufacture Taizhou University Taizhou318000 China

ISBN: (纸本)9798350361674

In this paper, a hyperbolic-secant reaching law-based sliding mode control method is presented for uncertain discrete-time controlled systems. the hyperbolic-secant reaching law based on the hyperbolic-secant function and the saturation function is designed to realize the increasing of the reaching speed and the reduction of the chattering. To characterize the tracking performance of the discrete controlled system, the detailed results about the absolute attractive layer bound and the quasi-sliding mode band are given and analyzed. the validity and usefulness of the proposed control approach can be verified by numerical simulation results. © 2024 ieee.

关键词： Sliding mode control

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Integral-Type Event-Triggered Model Predictive control for Manipulator systems 13

Integral-Type Event-Triggered Model Predictive Control for M...

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

作者： Liu, Shifeng Ren, Xuemei Wang, Pengbiao Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798350361674

In this paper, we study an integral-type event-triggered model predictive control method for manipulator systems. the event-triggered mechanism, which is based on integrating the error between the actual and predicted states, is applied to the system when the state is outside the terminal domain. In comparison to directly using the difference, the proposed method achieves a more effective balance between system performance and the frequency of solving optimization problems. Furthermore, the event-triggered strategy is also considered in the state-feedback controller to save computing resources. the feasibility and stability are rigorously analyzed. Finally, the simulation results of the manipulator system validate the feasibility and effectiveness of the proposed algorithm. © 2024 ieee.

关键词： Predictive control systems

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