Composite learning control of flexible-link manipulator with online recorded data and disturbance observer

被引：0
作者：
Qian, Wei [1 ]
Xu, Hai-Qin [1 ]
Wang, Xia [2 ]
Xu, Bin [2 ]
机构：
[1] School of Electrical Engineering and Automation, Henan Polytechnic University, Henan, Jiaozuo
[2] School of Automation, Northwestern Polytechnical University, Shaanxi, Xi’an
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2024年 / 41卷 / 08期
基金：
中国国家自然科学基金;
关键词：
composite learning control; disturbance observe; flexible-link manipulator; online recorded data;
D O I：
10.7641/CTA.2023.20590
中图分类号：
学科分类号：
摘要：
For the dynamics of multiple input multipe output (MIMO) flexible-link manipulator, this paper investigates a composite learning controller based on the neural networks (NN) and disturbance observer. Firstly, the system is decoupled into the fast and slow subsystems by singular perturbation analysis. Then for the slow-varying dynamics, a novel prediction error is constructed based on the online recorded data scheme. The update law for NN weights is designed by combining the tracking error. A sliding mode controller is constructed to suppress the flexible modes. Furthermore, a disturbance observer is built to estimate the compound disturbance, which is also used as the feedforward compensation of the online recorded data scheme. The boundedness of the system signals is proved via the Lyapunov approach. The simulation test illustrates the effectiveness and superiority of the proposed approach. © 2024 South China University of Technology. All rights reserved.
引用
收藏
页码：1417 / 1426
页数：9
相关论文
共 24 条
  • [1] YANG C, JIANG Y, HE W, Et al., Adaptive parameter estimation and control design for robot manipulators with finite-time convergence, IEEE Transactions on Industrial Electronics, 65, 10, pp. 8112-8123, (2018)
  • [2] WU Xinsheng, DENG Jun, Robust boundary control of a distributed parameter flexible manipulator with tip unknown disturbance, Control Theory & Applications, 28, 4, pp. 511-518, (2011)
  • [3] BASTOS G., A stable reentry trajectory for flexible manipulators, International Journal of Control, 94, 5, pp. 1297-1308, (2021)
  • [4] YANG X, GE S, HE W., Dynamic modelling and adaptive robust tracking control of a space robot with two-link flexible manipulators under unknown disturbances, International Journal of Control, 91, 4, pp. 969-988, (2018)
  • [5] SUN C, GAO H, HE W, Et al., Fuzzy neural network control of a flexible robotic manipulator using assumed mode method, IEEE Transactions on Neural Networks and Learning Systems, 29, 11, pp. 5214-5227, (2018)
  • [6] ZHANG S, HE W, HUANG D., Active vibration control for a flexible string system with input backlash, IET Control Theory and Applications, 10, 7, pp. 800-805, (2016)
  • [7] ALANDOLI E, LEE T, LIN Y, Et al., Dynamic model and intelligent optimal controller of flexible link manipulator system with payload uncertainty, Arabian Journal for Science and Engineer, 46, 8, pp. 7423-7433, (2021)
  • [8] CHEN X, FUKUDA T., Robust sliding-mode tip position control for flexible arms, IEEE Transactions on Industrial Electronics, 48, 6, pp. 1048-1056, (2001)
  • [9] SPONG M., Modeling and control of elastic joint robots, Journal of Dynamic Systems, Measurement, and Control, 109, 4, pp. 310-319, (1987)
  • [10] KIM J., Two-time scale control of flexible joint robots with an improved slow model, IEEE Transactions on Industrial Electronics, 65, 4, pp. 3317-3325, (2018)
123