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11th World Congress on Intelligent control and Automation

作者： Sun, Mingxuan Zhang, Guofeng Wu, Tao Zhejiang Univ Technol Coll Informat Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781479958252

In this paper, the problem of adaptive iterative learning control using neural networks is addressed by an error tracking approach for systems with arbitrary initial states. The desired error trajectory is pre-specified at the design stage. It is shown that the tracking error is ensured to converge to an adjustable neighborhood of a pre-specified one. The performance improvement is made possible in case of non-zero approximation error, due to the use of an appropriate Lyapunov functional adopted in the design.

关键词： Initial conditions adaptive iterative learning control neural networks nonlinear systems

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Global iterative learning control based on fuzzy systems for nonlinear multi-agent systems with unknown dynamics

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INFORMATION SCIENCES 2022年 587卷 556-571页

作者： Zhang, Shuai Chen, Jiaxi Bai, Chan Li, Junmin Xidian Univ Sci & Technol Antennas & Microwave Lab Xian 710071 Peoples R China Xidian Univ Sch Math & Stat Xian 710071 Peoples R China

A new global fuzzy iterative learning scheme is proposed for nonlinear multi-agent systems with unknown dynamics. Unlike the traditional design scheme where the fuzzy systems are used as the feedback compensators, the fuzzy systems are used as the feedforward compensators to describe the unknown dynamics, which avoids the restriction on the states of the control systems. In this scheme, we design a hybrid fuzzy adaptive learning controller according to the characteristics of the network structure. On this basis, using the Nussbaum function, this paper extends the above global fuzzy iterative learning scheme to solve the consensus control problem of multi-agent systems with unknown control directions over the iterations. Finally, the effectiveness of the above hybrid learning protocols is verified through simulations. (C) 2021 Elsevier Inc. All rights reserved.

关键词： adaptive iterative learning control Multi-agent systems Fuzzy systems Global consensus

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Filtering-Error Constrained Angle Tracking adaptive learning Fuzzy control for Pneumatic Artificial Muscle Systems Under Nonzero Initial Errors

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IEEE ACCESS 2022年 10卷 41828-41838页

作者： GUAN, X. I. A. O. H. U. I. HE, Z. H. O. N. G. J. I. E. ZHANG, M. E. I. Y. A. N. XIA, H. U. I. J. I. E. Zhejiang Univ Water Resources & Elect Power Coll Informat Engn Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China Zhejiang Univ Water Resources & Elect Power Coll Elect Engn Hangzhou 310018 Peoples R China

In this paper, a filtering-error constrained adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a pneumatic artificial muscle-actuated mechanism. The adaptive learning controller is designed by a novel barrier Lyapunov function, and the filtering error of pneumatic artificial muscle system is ensured to be constrained during each iteration. The initial position problem of iterative learning control is solved by utilizing time-varying boundary layer method. Fuzzy logic system is applied to approximate the unknown nonparametric uncertainties in the pneumatic artificial muscle system, whose optimal weight is estimated by using difference learning approach. The approximation error of fuzzy logic system is tackled by robust control strategy. Simulation results show the effectiveness of the propose angle tracking adaptive learning fuzzy control scheme.

关键词： Actuators Fuzzy logic Muscles adaptive learning Trajectory iterative learning control Voltage control Pneumatic artificial muscle systems adaptive iterative learning control barrier Lyapunov function initial position problem fuzzy logic systems

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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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Robust iterative learning control for Robot Manipulators with Input Deadzone 12

Robust Iterative Learning Control for Robot Manipulators wit...

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IEEE 12th Data Driven control and learning Systems Conference (DDCLS)

作者： Ji, Shuangjie Yan, Qiuzhen Chen, Qiang Lin, Mingjun 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

This work studies trajectory tracking problem for robot manipulators with actuator deadzone under arbitrary initial states. A robust iterative learning control scheme is developed according to Lyapunov theory. First, the rectification reference trajectory is constructed for handling initial position of robotic ILC systems. Then, based on system parameterization technique, a robust adaptive iterative learning control law is derived to dealing with the deadzone nonlinearity and uncertainties. The convergence performance of the system and the boundedness of the closed-loop signal are proved by theoretical analysis. At the end, a numerical simulation is carried out to demonstrate the effectiveness of the proposed robust learning control scheme.

关键词： adaptive iterative learning control nonparametric systems input deadzone inverse model

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Error tracking-driven adaptive backstepping ILC of nonlinear systems with initial shifts

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JOURNAL OF THE FRANKLIN INSTITUTE 2025年第2期362卷

作者： Liu, Jinyu Liu, Yang Chi, Ronghu Hou, Zhongsheng Qingdao Univ Sci & Technol Sch Automat & Elect Engn Qingdao 266061 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

High-order nonlinear systems can describe various practically physical systems, such as tower crane, robot manipulator, high-speed train, satellite system and complex network. Hence, it is important to investigate the control problems for such systems. Moreover, a high precision control objective is required in the applications of satellite positioning, metal cutting, chemical processing, etc. Therefore, under the framework of backstepping technique, this paper proposes an ETD-based adaptive ILC control towards achieving a perfect tracking performance. Furthermore, by using ETD and STD methods, the problems with regard to the identical initial condition and differential explosion caused by the consecutive derivation of virtual controller can be handled well. As a result, the proposed ETD-ABILC can be easily applied to practical systems.

关键词： adaptive iterative learning control Error tracking-driven approach Sigmoid tracking differentiator Backstepping

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adaptive ILC methods with less adaption parameters for non-parameterized nonlinear continuous systems with nonsingular control gain matrices

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INTERNATIONAL JOURNAL OF adaptive control AND SIGNAL PROCESSING 2024年第11期38卷 3656-3672页

作者： Ding, Ya-Qiong Li, Xiao-Dong Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 518107 Peoples R China

In this article, for non-parameterized nonlinear continuous (NPNC) multiple-input multiple-output (MIMO) systems, two combined iteration-domain and time-domain adaptive iterative learning control (ILC) algorithms are proposed to track iteration-varying reference trajectories repetitively over a finite time interval. Different from the general requirement in adaptive control community that the control gain matrices of the controlled systems are real symmetric and positive-definite, only the nonsingular property of the control gain matrices is assumed. Moreover, there are just two adaption parameters and one adaption parameter involved in the proposed two adaptive ILC algorithms respectively such that the computation load and memory-space are greatly saved. A simulation example is utilized to illustrate the effectiveness of the two proposed adaptive ILC algorithms with less adaption parameters.

关键词： adaption parameter adaptive iterative learning control iteration-varying reference trajectories non-parameterized nonlinear continuous systems nonsingular control gain matrix

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Fully distributed coordination learning control of second-order nonlinear multi-agent systems with input saturation

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ASIAN JOURNAL OF control 2021年第4期23卷 1748-1761页

作者： Yang, Nana Li, Junmin Xidian Univ Sch Math & Stat Xian 710126 Peoples R China Lanzhou Univ Technol Sch Sci Lanzhou Peoples R China

This paper develops the coordination learning control of second-order non-linear multi-agent systems with input saturation. Fully distributed learning consensus protocols with fully saturated parameter adaptive updating laws are designed. Although there exists input saturation in the dynamics of each follower agent, the global perfect consensus tracking can be realized over a finite time interval. Meanwhile, an extension to formation control has been made, and two illustrative examples certify the validness of the presented algorithms.

关键词： adaptive iterative learning control consensus tracking formation control input saturation multi-agent systems

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Time-Varying Boundary Layer Based iterative learning control for Nonlinearly Parametric Time-Delay Systems With Arbitrary Initial Errors and Iteration-Varying Reference Trajectories

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IEEE ACCESS 2021年 9卷 90642-90655页

作者： Wu, Lingwei Yu, Youfang Lin, Zhiming Yan, Qiuzhen Liu, Xiangbin Taizhou Univ Coll Elect & Informat Engn Taizhou 318000 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Zhejiang Business Coll Appl Engn Coll Hangzhou 310053 Peoples R China Zhejiang Univ Water Resources & Elect Power Coll Informat Engn Hangzhou 310018 Peoples R China Beijing Jiaotong Univ Sch Elect Informat Engn Beijing 100044 Peoples R China

In this paper, the tracking problem for a class of nonlinearly parametric time-delay systems with arbitrary initial state errors and iteration-varying reference trajectories is studied. An adaptive iterative learning control scheme is developed by using Lyapunov approach. A time-varying boundary layer is constructed for removing the zero initial error condition, which is necessary in normal iterative learning control algorithms. The time-varying nonlinearly parametric uncertainties and time-delay uncertainties are well compensated by using parameter separation technique, signal replacement mechanism and robust strategy. As the iteration number increases, the state tracking error asymptotically converges to a tunable residual set. In the end, a simulation example further verifies the theoretical results.

关键词： Trajectory adaptive systems Time-varying systems Uncertainty iterative learning control iterative algorithms Delay effects adaptive iterative learning control time delay nonlinearly parametric systems initial position problem time-varying boundary layer

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A unified adaptive control approach of nonlinear continuous non-parameterized systems with asymmetric control gains for trajectory tracking in different domains

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2023年第7期33卷 3964-3987页

作者： Ding, Ya-Qiong Li, Xiao-Dong Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510006 Peoples R China Guangzhou Xinhua Univ Sch Informat & Intelligent Engn Dongguan Peoples R China

In this article, for nonlinear continuous non-parameterized (NCNP) systems, an adaptive iterative learning control (ILC) algorithm, which can adjust the adaptive parameters in both iteration-domain and time-domain, is first proposed to track different reference trajectories repetitively over a finite time interval. As the NCNP system is required to asymptotically track reference trajectory in infinite time-domain, by virtue of partitioning the reference trajectory and system signals with a fixed time interval, the proposed adaptive ILC controller is then extended to handle the asymptotic tracking issue in infinite time-domain. Therefore, a unified adaptive control approach is practically presented for NCNP systems to track reference trajectories in different domains (the infinite iteration-domain and the infinite time-domain). A prominent feature of the unified adaptive control approach is that the unknown control gain matrices in the NCNP systems are assumed to be invertible only. As a result, the general requirement in conventional adaptive control and adaptive ILC that the control gain matrices of plants are real symmetric and positive-definite (or negative-definite) is greatly relaxed. In addition, only three adaptive variables are designed for adjusting or updating in the proposed unified adaptive control approach such that the structure of controller is very simple and the memory space for computing is saved.

关键词： adaptive iterative learning control asymmetric control gain nonlinear continuous non-parameterized systems Nussbaum gain reference trajectory tracking

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