ITERATIVE_LEARNING_CONTROL

作品数：104被引量：259H指数：9
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Dynamic event-triggered data-driven iterative learning bipartite tracking control for nonlinear MASs with prescribed performance
《Science China(Information Sciences)》2025年第1期289-302,共14页Tao SHI Wei-Wei CHE 
supported by National Natural Science Foundation of China(Grant Nos.U1966202,61873338,62273191,62233015);Taishan Scholars(Grant No.tsqn201812052);Natural Science Foundation of Shandong Province(Grant No.ZR2020KF034)。
This article proposes a distributed dynamic event-triggered data-driven iterative learning control(DET-DDILC)scheme under a predefined performance to tackle the bipartite tracking control problem for multiagent system...
关键词：bipartite tracking control prescribed performance event-triggered iterative learning control multiagent systems 
A Modified Iterative Learning Control Approach for the Active Suppression of Rotor Vibration Induced by Coupled Unbalance and Misalignment
《Chinese Journal of Mechanical Engineering》2024年第1期242-253,共12页Yifan Bao Jianfei Yao Fabrizio Scarpa Yan Li 
Supported by National Natural Science Foundation of China(Grant Nos.51975037,52375075).
This paper proposes a modified iterative learning control(MILC)periodical feedback-feedforward algorithm to reduce the vibration of a rotor caused by coupled unbalance and parallel misalignment.The control of the vibr...
关键词：Rotor vibration suppression Modified iterative learning control UNBALANCE Parallel misalignment Active magnetic actuator 
Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems
《IEEE/CAA Journal of Automatica Sinica》2024年第2期344-361,共18页Yunfeng Hu Chong Zhang Bo Wang Jing Zhao Xun Gong Jinwu Gao Hong Chen 
supported by the National Natural Science Foundation of China(U21A20166);in part by the Science and Technology Development Foundation of Jilin Province (20230508095RC);in part by the Development and Reform Commission Foundation of Jilin Province (2023C034-3);in part by the Exploration Foundation of State Key Laboratory of Automotive Simulation and Control。
Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning ...
关键词：Adaptive control control system synthesis data-driven iterative learning control neurocontroller nonlinear discrete time systems 
Neural networks-based iterative learning control consensus for periodically time-varying multi-agent systems
《Science China(Technological Sciences)》2024年第2期464-474,共11页CHEN JiaXi LI JunMin CHEN WeiSheng GAO WeiFeng 
supported by the National Natural Science Foundation of China(Grant Nos.62203342,62073254,92271101,62106186,and62103136);the Fundamental Research Funds for the Central Universities(Grant Nos.XJS220704,QTZX23003,and ZYTS23046);the Project funded by China Postdoctoral Science Foundation(Grant No.2022M712489);the Natural Science Basic Research Program of Shaanxi(Grant Nos.2023-JC-YB-585 and 2020JM-188)。
In this paper,the problem of adaptive iterative learning based consensus control for periodically time-varying multi-agent systems is studied,in which the dynamics of each follower are driven by nonlinearly parameteri...
关键词：multi-agent systems adaptive iterative learning control nonlinearly parameterized dynamics Fourier series expansion neural networks 
Data-Driven Learning Control Algorithms for Unachievable Tracking Problems被引量：1
《IEEE/CAA Journal of Automatica Sinica》2024年第1期205-218,共14页Zeyi Zhang Hao Jiang Dong Shen Samer S.Saab 
supported by the National Natural Science Foundation of China (62173333, 12271522);Beijing Natural Science Foundation (Z210002);the Research Fund of Renmin University of China (2021030187)。
For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to in...
关键词：Data-driven algorithms incomplete information iterative learning control gradient information unachievable problems 
Fundamental Trackability Problems for Iterative Learning Control
《IEEE/CAA Journal of Automatica Sinica》2023年第10期1933-1950,共18页Deyuan Meng Jingyao Zhang 
supported in part by the National Natural Science Foundation of China (62273018);in part by the Science and Technology on Space Intelligent Control Laboratory (HTKJ2022KL502006)。
Generally, the classic iterative learning control(ILC)methods focus on finding design conditions for repetitive systems to achieve the perfect tracking of any specified trajectory,whereas they ignore a fundamental pro...
关键词：CONVERGENCE functional Cauchy sequence(FCS) iterative learning control(ILC) trackability 
Iteration dependent interval based open‐closed‐loop iterative learning control for time varying systems with vector relative degree
《CAAI Transactions on Intelligence Technology》2023年第3期645-660,共16页Yun‐Shan Wei Jin‐Fan Wang Jia‐Xuan Wang Qing‐Yuan Xu Jaime Lloret 
supported in part by the National Natural Science Foundation of China of No.61903096;Guangzhou Key Laboratory of Software‐Defined Low Latency Network of No.202102100006;Guangdong Basic and Applied Basic Research Foundation of No.2020A1515110414.
For linear time varying(LTV)multiple input multiple output(MIMO)systems with vector relative degree,an open‐closed‐loop iterative learning control(ILC)strategy is developed in this article,where the time interval of...
关键词：intelligent control iterative methods 
Data-driven iterative learning trajectory tracking control for wheeled mobile robot under constraint of velocity saturation
《IET Cyber-Systems and Robotics》2023年第2期37-47,共11页Xiaodong Bu Xisheng Dai Rui Hou 
supported by the Innovation Project of Guangxi Graduate Education(Grant No.YCSW2022436).
Considering the wheeled mobile robot(WMR)tracking problem with velocity saturation,we developed a data‐driven iterative learning double loop control method with constraints.First,the authors designed an outer loop co...
关键词：data-driven control dynamic model iterative learning control trajectory tracking velocity saturation wheeled mobile robot 
Enhancing Iterative Learning Control With Fractional Power Update Law被引量：1
《IEEE/CAA Journal of Automatica Sinica》2023年第5期1137-1149,共13页Zihan Li Dong Shen Xinghuo Yu 
supported by the National Natural Science Foundation of China(62173333);Australian Research Council Discovery Program(DP200101199)。
The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategi...
关键词：Asymptotic convergence convergence rate finiteiteration tracking fractional power learning rule limit cycles 
Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence
《Computer Modeling in Engineering & Sciences》2023年第1期343-355,共13页Zhiguo Wang Fangqing Gao Fei Liu 
In this paper,an improved high-order model-free adaptive iterative control(IHOMFAILC)method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method.This meth...
关键词：Pseudo partial derivative enhanced convergence tracking error disturbance compensation 
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