Adaptive Command Filtered Control of Strict Feedback Systems With Uncertain Control Gains

被引：0
|
作者：
Wu J.-W. [1 ,2 ,3 ]
Liu Y.-H. [1 ,2 ,3 ]
Su C.-Y. [1 ,2 ,3 ]
Lu R.-Q. [1 ,2 ,3 ]
机构：
[1] School of Automation, Guangdong University of Technology, Guangzhou
[2] Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control, Guangzhou
[3] Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangzhou
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2024年 / 50卷 / 05期
基金：
中国国家自然科学基金;
关键词：
adaptive control; command filtered backstepping; neural network control; Nonlinear systems;
D O I：
10.16383/j.aas.c210553
中图分类号：
学科分类号：
摘要：
In this paper, a command filtered-based adaptive neural control scheme is developed for strict feedback systems with uncertain control gains. In the developed scheme, neural networks are adopted to approximate the unknown nonlinear system functions and command filtered backstepping technique is utilized to solve the “explosion of complexity” problem. Compared with the literature on command filtered backstepping control, in this paper, an adaptive error compensating system is constructed to eliminate the impacts of the boundary layer errors generated by the filters and the uncertain control gains on system performance simultaneously. Simulation results are presented to verify the effectiveness of the proposed control scheme. © 2024 Science Press. All rights reserved.
引用
收藏
页码：1015 / 1023
页数：8
相关论文
共 40 条
  • [1] Edwards C, Spurgeon S., Sliding Mode Control, (1998)
  • [2] Isidori A., Nonlinear Control Systems, (1995)
  • [3] Liu Y H, Su C Y, Li H, Lu R., Barrier function-based adaptive control for uncertain strict-feedback systems within predefined neural network approximation sets, IEEE Transactions on Neural Networks and Learing Systems, 31, 8, pp. 2942-2954, (2020)
  • [4] Lin W, Zhang X., A dynamic feedback framework for control of time-delay nonlinear systems with unstable zero dynamics, IEEE Transactions on Automatic Control, 65, 8, pp. 3317-3332, (2020)
  • [5] Min H, Xu S, Zhang Z., Adaptive finite-time stabilization of stochastic nonlinear systems subject to full-state constraints and input saturation, IEEE Transactions on Automatic Control, 66, 3, pp. 1306-1313, (2021)
  • [6] Wang C, Wang M, Liu T, Hill D J., Learning from ISS-Modular adaptive NN control of nonlinear strict-feedback systems, IEEE Transactions on Neural Networks and Learning Systems, 23, 10, pp. 1539-1550, (2012)
  • [7] Theodorakopoulos A, Rovithakis G A., Guaranteeing preselected tracking quality for uncertain strict-feedback systems with dead-zone input nonlinearity and disturbances via low-complexity control, Automatica, 54, pp. 135-145, (2015)
  • [8] Zhai D, An L W, Dong J X, Zhang Q L., Output feedback adaptive sensor failure compensation for a class of parametric strict feedback systems, Automatica, 97, pp. 48-57, (2018)
  • [9] Zhao K, Song Y D., Removing the feasibility conditions imposed on tracking control designs for state-constrained strict-feedback systems, IEEE Transactions on Automatic Control, 64, 3, pp. 1265-1272, (2019)
  • [10] Zhou Qi, Chen Guang-Deng, Lu Ren-Quan, Bai Wei-Wei, Disturbance-observer-based event-triggered control for multi-agent systems with input saturation, Scientia Sinica Informationis, 49, 11, pp. 1502-1516, (2019)
1234