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11th World Congress on Intelligent control and Automation

作者： Zhang, Li Liu, Shan Zhejiang Univ Dept Control Sci & Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781479958252

Perfect tracking of the flexible-link manipulator (FLM) tip position has not been achieved by causal control because the FLM is a typical non-minimum phase system. Combined with non-causal stable inversion, an adaptive iterative learning control (ILC) scheme based on Fourier basis functions is presented for the tip trajectory tracking of the repeat running FLM. In this method, an iterative identification algorithm is used to calculate the basis function space model of the manipulator, and a pseudo-inverse type ILC law is designed to approximate the stable inversion of the system, which guarantees the convergence and robustness of the control system. Simulation results show the performance and effectiveness of the proposed scheme.

关键词： Flexible-link manipulator Non-minimum phase system adaptive iterative learning control Fourier basis function stable inversion

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adaptive iterative learning control of non-linear MIMO continuous systems with iteration-varying initial error and reference trajectory

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2013年第4期44卷 786-794页

作者： Li, Xiao-Dong Chow, Tommy W. S. Cheng, L. L. Sun Yat Sen Univ Sch Informat Sci & Technol Key Lab Machine Intelligence & Sensor Network Minist Educ Guangzhou 510006 Guangdong Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

In this article, an adaptive iterative learning control (ILC) approach is presented to deal with a class of non-linear multi-input multi-output (MIMO) continuous systems with parametric uncertainty. Unlike general ILC techniques, the proposed adaptive ILC approach allows that both the initial error and the reference trajectory are iteration-varying in the ILC process. The designed ILC tracking strategy is to set out a very small initial time interval, and track the iteration-varying reference trajectory beyond the initial time interval. The reference trajectory tracking error beyond the initial time interval can be driven to zero. While the proposed adaptive ILC technique is applied to the repetitive learning control of non-linear MIMO continuous systems, a complete reference trajectory tracking over the whole time interval can be achieved.

关键词： adaptive iterative learning control non-linear MIMO continuous systems iteration-varying initial error iteration-varying reference trajectory

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adaptive iterative learning control OF ROBOTIC SYSTEMS USING BACKSTEPPING DESIGN

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TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2013年第3期37卷 591-601页

作者： Wang, Ying-Chung Chien, Chiang-Ju Chuang, Chi-Nan Huafan Univ Dept Elect Engn New Taipei City Taiwan

In this paper, a backstepping adaptive iterative learning control (AILC) is proposed for robotic systems with repetitive tasks. The AILC is designed to approximate unknown certainty equivalent controller. Finally, we apply a Lyapunov like analysis to show that all adjustable parameters and the internal signals remain bounded for all iterations.

关键词： robotic systems backstepping adaptive iterative learning control fuzzy neural network

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Study of a MEMS Vibratory Gyroscope Using adaptive iterative learning control

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2014年第9期11卷 151-151页

作者： Lu, Xiaochun Fei, Juntao Hohai Univ Coll IOT Engn Changzhou Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing Peoples R China

This paper proposes a framework, namely adaptive iterative learning control (AILC), which is used in the control of a microelectromechanical system (MEMS) gyroscope, to realize high-precision trajectory tracking control. According to the characteristics of the MEMS gyroscope's model, the proposed AILC algorithm includes an adaptive law of parametric estimation and an iteration control law, which is updated in the iterative domain without any prior knowledge of MEMS gyroscopes. The convergence of the method is proven by a Lyapunov-like approach, which shows that the designed controller can guarantee the stability of the system and make the output tracking errors to converge completely to zero while the iteration index tends to infinity. By comparing AILC and traditional PD-ILC, the simulation results demonstrate the effectiveness of AILC and its robustness against external random disturbance.

关键词： MEMS Vibratory Gyroscopes adaptive iterative learning control Trajectory Tracking adaptive control

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adaptive learning control for group consensus tracking of discrete nonlinear multiagent systems

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ASIAN JOURNAL OF control 2025年第2期27卷 863-875页

作者： Gao, Qianhui Li, Jinsha Li, Junmin Xidian Univ Sch Math & Stat Xian 710071 Peoples R China

In this article, we explore the group output tracking consensus problem for discrete-time strict-feedback n$$ n $$-order nonlinear multiagent systems that run repeatedly on finite time {0,& mldr;,T}$$ \left\{0,\dots, T\right\} $$. A novel distributed adaptive iterative learning group consensus protocol is designed, which consists of two main components. The first component is based on time-varying neural networks, which is used to approximate the unknown nonlinear function in the n$$ n $$-step ahead predictor. In general, not all followers can access the information regarding the leader, which complicates the design of iterative learning protocols for MASs. Therefore, the second component of the protocol addresses this challenge by treating the leader's output as a time-varying parameter and designing a time-varying auxiliary term to compensate the leader's output information. Parameter updating laws and initial state learning laws are also proposed via the cooperative-competitive relationship between the agents. We demonstrate the group consensus with sufficient small errors can be achieved at time {n,& mldr;,T}$$ \left\{n,\dots, T\right\} $$, as the number of iterations proceed to infinity. Then, the results are extended to the case of multisubgroups and multileaders. Finally, two simulations validate the findings of this article.

关键词： adaptive iterative learning control discrete-time nonlinear MASs group consensus tracking neural networks strict-feedback

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Neural networks-based iterative learning control consensus for periodically time-varying multi-agent systems

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Science China(Technological Sciences) 2024年第2期67卷 464-474页

作者： CHEN JiaXi LI JunMin CHEN WeiSheng GAO WeiFeng School of Mathematics and Statistics Xidian UniversityXi'an 710071China School of Aerospace Science and Technology Xidian UniversityXi'an 710071China

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 parameterized terms with periodic *** networks and Fourier base expansions are introduced to describe the periodically time-varying dynamic *** this basis,an adaptive learning parameter with a positively convergent series term is constructed,and a distributed control protocol based on local signals between agents is designed to ensure accurate consensus of the closed-loop ***,consensus algorithm is generalized to solve the formation control ***,simulation experiments are implemented through MATLAB to demonstrate the effectiveness of the method used.

关键词： multi-agent systems adaptive iterative learning control nonlinearly parameterized dynamics Fourier series expansion neural networks

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adaptive fuzzy iterative learning control with initial-state learning for coordination control of leader-following multi-agent systems

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FUZZY SETS AND SYSTEMS 2014年 248卷 122-137页

作者： Li, Junmin Li, Jinsha Xidian Univ Dept Appl Math Xian 710071 Peoples R China

We propose a distributed adaptive fuzzy iterative learning control (ILC) algorithm to deal with coordination control problems in leader-following multi-agent systems in which each follower agent has unknown dynamics and a non-repeatable input disturbance. The ILC protocols are designed with distributed initial-state learning and it is not necessary to fix the initial value at the beginning of each iteration. A fuzzy logical system is used to approximate the nonlinearity of each follower agent. A fuzzy learning component is an important learning tool in the protocol, and combined time-domain and iteration-domain adaptive laws are used to tune the controller parameters. The protocol guarantees that the follower agents track the leader for the consensus problem and keep at a desired distance from the leader for the formation problem on [0, T]. Simulation examples illustrate the effectiveness of the proposed scheme. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Multi-agent system Fuzzy system adaptive iterative learning control Nonlinear system

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Consensus control for heterogeneous uncertain multi-agent systems with hybrid nonlinear dynamics via iterative learning algorithm

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Science China(Technological Sciences) 2023年第10期66卷 2897-2906页

作者： XIE Jin CHEN JiaXi LI JunMin CHEN WeiSheng ZHANG Shuai School of Mathematics and Statistics Xidian UniversityXi’an 710071China School of Aerospace Science and Technology Xidian UniversityXi’an 710071China Science and Technology on Antennas and Microwave Laboratory Xidian UniversityXi’an 710071China

In this study,We propose a compensated distributed adaptive learning algorithm for heterogeneous multi-agent systems with repetitive motion,where the leader's dynamics are unknown,and the controlled system's parameters are *** multiagent systems are considered a kind of hybrid order nonlinear systems,which relaxes the strict requirement that all agents are of the same order in some existing *** theoretical analyses,we design a composite energy function with virtual gain parameters to reduce the restriction that the controller gain depends on global *** the stability of the controller,we introduce a smooth continuous function to improve the piecewise controller to avoid possible *** analyses prove the convergence of the presented algorithm,and simulation experiments verify the effectiveness of the algorithm.

关键词： multi-agent systems adaptive iterative learning control hybrid nonlinear dynamics composite energy function consensus algorithm

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adaptive iteration learning control with iteration-varying event-triggered mechanism for discrete-time nonlinear systems with random initial states

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2023年第11期33卷 6135-6150页

作者： Chai, Sheng Yu, Miao Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

In this work, a novel adaptive event-trigered iterative learning control (ILC) scheme is designed for discrete-time nonlinear systems with time-varying parameters and random initial states. The reference trajectory can be iteration-invariant or iteration-varying. The control input is updated when the event-triggering condition is satisfied, which reduces the action time of controller and consequently the unnecessary waste of communication resource. An adaptive event-triggering condition parameter is designed to improve the efficiency of the event generator. Moreover, the parameter updating law of adaptive iterative learning controller is designed based on the recursive least squares algorithm. With the proposed adaptive ILC with event-triggered mechanism, the learning convergence along the iteration axis is guaranteed and communication burden is reduced. Finally, rigorous theoretical analysis under Lyapunov theory and illustrative examples are given to demonstrate the efficacy of the proposed method.

关键词： adaptive iterative learning control discrete-time nonlinear systems event-triggered control Lyapunov function non-repetitiveness parametric uncertainty

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Enhanced P-Type control: Indirect adaptive learning From Set-Point Updates

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IEEE TRANSACTIONS ON AUTOMATIC control 2023年第3期68卷 1600-1613页

作者： Chi, Ronghu Li, Huaying Shen, Dong Hou, Zhongsheng Huang, Biao Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada

In this article, an indirect adaptive iterative learning control (iAILC) scheme is proposed for both linear and nonlinear systems to enhance the P-type controller by learning from set points. An adaptive mechanism is included in the iAILC method to regulate the learning gain using input-output measurements in real time. An iAILC method is first designed for linear systems to improve control performance by fully utilizing model information if such a linear model is known exactly. Then, an iterative dynamic linearization (IDL)-based iAILC is proposed for a nonlinear nonaffine system, whose model is completely unknown. The IDL technique is employed to deal with the strong nonlinearity and nonaffine structure of the systems such that a linear data model can be attained consequently for the algorithm design and performance analysis. The convergence of the developed iAILC schemes is proved rigorously, where contraction mapping, two-dimensional (2-D) Roesser's system theory, and mathematical induction are employed as the basic analysis tools. Simulation studies are provided to verify the developed theoretical results.

关键词： Convergence Uncertainty Analytical models adaptive systems Linear systems Data models Tuning adaptive iterative learning control convergence analysis P-type controller set-point updating method

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