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39th Chinese control Conference (CCC)

作者： Xu, Jiahui Li, Dazi Ma, Xin Zhang, Beike Jin, Qibing Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

ISBN: (纸本)9789881563903

In this paper, an adaptive iterative earning control algorithm is designed based on extended state observer. Traditional robotic path tracking control has low control precision and poor control performance. A basic structure of the double joint manipulator is used for study. The system model is established, and a manipulator trajectory tracking control scheme is proposed. The specific algorithm principle and implementation method are given. The proposed algorithm consists of variable gain PD control, adaptive control, and extended state observer. The PD control can change the control gain adaptively because of the adaptive feedback loop to ensure the system stability. The extended state observer regards both the internal interference and the external random disturbance of the system. The state is used to compensate for the uncertainty of both. Experiment results show that the proposed algorithm improves the range and performance of the trajectory tracking error while improving the system convergence, and improves the control performance of the system.

关键词： adaptive iterative learning control Extended State Observer Robot Path Tracking

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Robust Convergence of High-Order adaptive iterative learning control Against Iteration-Varying Uncertainties

Robust Convergence of High-Order Adaptive Iterative Learning...

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2019 Chinese Intelligent Systems Conference

作者： Guo, Zirong Meng, Deyuan Zhang, Jingyao Beihang Univ BUAA Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beihang Univ BUAA Seventh Res Div Beijing 100191 Peoples R China

ISBN: (纸本)9789813296848;9789813296824;9789813296817

In this paper, adaptive iterative learning control (AILC) algorithms using a high-order law for linear time-varying (LTV) systems with iteration-varying uncertainties are proposed. Sufficient conditions are derived to guarantee the robust convergence of tracking error such that the bounded-input-bounded-output stability of LTV systems can be achieved. Extensions of established AILC results to nonlinear systems are further developed. Numerical simulations are implemented to validate the effectiveness of the theoretical results.

关键词： adaptive iterative learning control Linear time-varying system Iteration-varying uncertainties

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adaptive Fuzzy iterative learning control for High-Speed Trains With Both Randomly Varying Operation Lengths and System Constraints

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2021年第8期29卷 2408-2418页

作者： Yu, Qiongxia Hou, Zhongsheng Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454000 Henan Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

In this article, a new adaptive fuzzy iterative learning control (AFILC) method is proposed for the tracking control of nonlinear uncertain high-speed train (HST) operation systems that have both randomly varying iteration lengths and speed and input force constraints. To cope with unknown time-varying basic resistance coefficients, an adaptive learning control law and two fully projected parameter learning laws are designed. The nonparametric and unknown additional resistance in the HST operation system is compensated and integrated into the control law by means of a newly constructed adaptive iterative learning fuzzy system. Moreover, due to the complex operation environment and various uncertainties, disturbances and emergencies, trains are often early or late compared with the prearranged timetable rather than being strictly on time in the repeated operations of each day, which leads to randomly varying operation lengths for actual HST running. Furthermore, as the traveling speed of modern HSTs increases, both the train's speed and input force should be constrained to guarantee the safe operation. Fortunately, the proposed AFILC can not only actively manipulate the position, speed, and input force of the train into prespecified and constrained ranges for safe operation but can also make both the position and speed tracking control errors converge to zero over the whole desired and scheduled time interval, even if the actual time interval varies in each operation of the HST. Simulations on a practical train operation system similar to China Railway High-speed (CRH)-3 train are further presented to demonstrate the applicability and effectiveness of the proposed method.

关键词： Resistance Uncertainty adaptive systems Fuzzy systems Force iterative learning control Indexes adaptive iterative learning control fuzzy iterative learning control system constraints varying iteration length

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adaptive iterative learning control for Upper Limb Rehabilitation Robots With Considering Model Uncertainty

Adaptive Iterative Learning Control for Upper Limb Rehabilit...

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第30届中国过程控制会议（CPCC 2019）

作者： Yafeng Li Aimin An Jinlei Wang Fancheng Meng College of Electrical and Information Engineering Lanzhou University of Technology Key Laboratory of Gansu Advanced Control for Industrial Processes

For the upper limb rehabilitation robot, the model uncertainty and spasmodic disturbance are the main factors that affect the tracking accuracy performance in trajectory tracking *** the repetitive features of the rehabilitation robot system, an adaptive iterative learning control algorithm that combined the adaptive mechanism and the unique properties of iterative learning algorithms is proposed to improve the dynamic tracking accuracy in the control of an upper limb rehabilitation *** Lyapunov stability theory, the global incremental stability of closed-loop systems is *** simulation results show that the proposed adaptive iterative learning control has good control performance in the case of system model uncertainty and external uncertainty disturbance, and satisfactory tracking performance can also be obtained.

关键词： Rehabilitation robot Model uncertainty Track tracking adaptive iterative learning control

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Robust adaptive iterative learning control for Nonlinear Systems with Non-Repetitive Variables 14

Robust Adaptive Iterative Learning Control for Nonlinear Sys...

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14th International Conference on Computer Science and Education (ICCSE)

作者： Zhou, Wei Liu, Baobin Jiangsu Vocat Inst Commerce Sch Intelligent Engn Technol Nanjing Peoples R China

ISBN: (纸本)9781728118468

In this work, the temporally and iteratively varying problems in iterative learning control for a class of nonlinear multiple input multiple output systems is discussed. Time-iteration-varying variables are generated by high-order internal models. Reference trajectories and system initial states are bounded and vary randomly in iteration domain. Then an operator is applied to update the estimation matrix for the whole uncertainties including non-repetitive parameters and time -varying disturbances. With the proposed adaptive iterative learning control technique, estimation error is bounded and tracking error converges to zero asymptotically. The effectiveness of the proposed control is verified through simulation study.

关键词： adaptive iterative learning control robust control non-repetitive variable high-order internal model nonlinear system

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Leader-following consensus of multi-agent systems with uncertain weight coupling topological graph using distributed adaptive iterative learning control 38

Leader-following consensus of multi-agent systems with uncer...

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38th Chinese control Conference (CCC)

作者： Yang, Nana Li, Junmin Chen, Jiaxi Lanzhou Univ Technol Sch Sci Lanzhou 730050 Peoples R China Xidian Univ Sch Math & Stat Xian 710071 Peoples R China

ISBN: (纸本)9789881563972

In this paper, the distributed leader-following consensus problem of multi-agent systems with uncertain topological graph is formulated from the viewpoint of two dimensional systems over a finite time interval. Combining with iterative learning control, the fully distributed consensus protocols with the adaptive updating laws of unknown time-varying coupling strengthes appeared in the communication topology are proposed for linear multi-agent systems. By choosing appropriate Lyapunov-Krasovskii functional, the leader-following consensus can be achieved on [0, T] for all undirected connected topology graph when the number of iterations tends to infinity. Finally, a simulation example is given to demonstrate the effectiveness of theoretical results.

关键词： Leader-following consensus Multi-agent systems adaptive iterative learning control Unknown time-varying coupling strengths Lyapunov-Krasovskii functional

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Study of an adaptive iterative learning control Based on Extended State Observer in Path Tracking of Double Joint Robot

Study of an Adaptive Iterative Learning Control Based on Ext...

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第三十九届中国控制会议

作者： Jiahui Xu Dazi Li Xin Ma Beike Zhang Qibing Jin Collage of Information Science and Technology Beijing University of Chemical Technology

In this paper, an adaptive iterative learning control algorithm is designed based on extended state observer. Traditional robotic path tracking control has low control precision and poor control performance. A basic structure of the double joint manipulator is used for study. The system model is established, and a manipulator trajectory tracking control scheme is proposed. The specific algorithm principle and implementation method are given. The proposed algorithm consists of variable gain PD control, adaptive control, and extended state observer. The PD control can change the control gain adaptively because of the adaptive feedback loop to ensure the system stability. The extended state observer regards both the internal interference and the external random disturbance of the system. The state is used to compensate for the uncertainty of both. Experiment results show that the proposed algorithm improves the range and performance of the trajectory tracking error while improving the system convergence, and improves the control performance of the system.

关键词： adaptive iterative learning control Extended State Observer Robot Path Tracking

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adaptive iterative learning control of a class of nonlinear time-delay systems with unknown backlash-like hysteresis input and control direction

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ISA TRANSACTIONS 2017年 70卷 79-92页

作者： Wei, Jianming Zhang, Youan Sun, Meimei Geng, Baoliang Naval Aeronaut & Astronaut Univ Dept Control Engn Yantai 264001 Peoples R China Yantai Nanshan Univ Dept Elect & Elect Engn Yantai 265713 Peoples R China

This paper presents an adaptive iterative learning control scheme for a class of nonlinear systems with unknown time-varying delays and control direction preceded by unknown nonlinear backlash-like hysteresis. Boundary layer function is introduced to construct an auxiliary error variable, which relaxes the identical initial condition assumption of iterative learning control. For the controller design, integral Lyapunov function candidate is used, which avoids the possible singularity problem by introducing hyperbolic tangent funciton. After compensating for uncertainties with time-varying delays by combining appropriate Lyapunov-Krasovskii function with Young's inequality, an adaptive iterative learning control scheme is designed through neural approximation technique and Nussbaum function method. On the basis of the hyperbolic tangent function's characteristics, the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function (CEF) in two cases, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach. (C) 2017 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： adaptive iterative learning control Nonlinear time-varying systems Backlash-like hysteresis Unknown control direction Unknown time-varying delays

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adaptive iterative learning control for Freeway Traffic Flow Systems Using Improved Bacterial Foraging Optimized Desired Traffic Densities Planning

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2017年第5期19卷 1492-1511页

作者： Wang, Ying-Chung Chien, Chiang-Ju Chi, Ronghu Hou, Zhongsheng Lee, Ching-Hung Huafan Univ Dept Elect Engn New Taipei Taiwan Qingdao Univ Sci & Technol Sch Automat & Elect Engn Qingdao Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China Natl Chung Hsing Univ Dept Mech Engn Taichung Taiwan

In this paper, we propose a discrete fuzzy-neural adaptive iterative learning control (AILC) for freeway traffic flow systems with random initial resetting errors, iteration-varying desired traffic densities, and random bounded off-ramp traffic volumes using traffic densities, space mean speeds, and on-ramp waiting queues design. It is assumed that the system nonlinear functions and input gains are unknown for controller design. An adaptive fuzzy-neural network controller and an adaptive robust controller are applied to compensate for the unknown system nonlinearities and input gains, respectively. Moreover, to deal with the disturbances from random bounded off-ramp traffic volumes, a dead-zone like auxiliary error with a time-varying boundary layer is introduced as a bounding parameter. This proposed auxiliary error is also utilized to construct the adaptive laws without using the bound of the input gain. The traffic density tracking error is shown to converge along the axis of learning iteration to a residual set whose level of magnitude depends on the width of boundary layer. Besides, since the nice desired traffic densities designed for the coordinated control objective of the AILC for freeway traffic flow systems are generally unknown, the improved bacterial forging optimization (IBFO) algorithm is used to optimize the fitness function, which is constructed by the coordinated control objective including (1) minimum total travel time, (2) minimum on-ramp average waiting time, and (3) minimum changes of desired traffic densities. Finally, a computer simulation example is used to verify the learning performance of the proposed fuzzy-neural AILC for freeway traffic flow systems using IBFO-based desired traffic densities planning.

关键词： adaptive iterative learning control Freeway traffic flow systems Fuzzy-neural network Desired traffic densities planning Improved bacterial forging optimization

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An adaptive iterative learning control approach based on disturbance estimation for manipulator system

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2019年第3期16卷

作者： Liu, Keping Chai, Yuanyuan Sun, Zhongbo Li, Yan Changchun Univ Technol Sch Elect & Elect Engn Changchun 130012 Jilin Peoples R China Jilin Univ Minist Educ Key Lab Bion Engn Changchun Jilin Peoples R China

An adaptive iterative learning control approach based on disturbance estimation has been developed for trajectory tracking of manipulators with uncertain parameters and external disturbances. The external disturbances are estimated by the feedback iterative learning method, whereas the uncertain parameters are compensated by adaptive control. This approach which is based on the disturbance estimation technique provides a rapid convergence of trajectory tracking errors. According to the Lyapunov theory, the sufficient condition of the asymptotic stability has been developed for the 2-degrees of freedom (DOFs) manipulator system. The numerical results show that the adaptive iterative learning control approach based on disturbance estimation is feasible and effective for the 2-DOFs manipulator. A comparison of the adaptive iterative learning control method and the iterative learning control method is completed, which shows that the adaptive iterative learning control method performs a faster convergence of the disturbance to the steady state.

关键词： adaptive iterative learning control disturbance estimation trajectory tracking Lyapunov function manipulator system

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