广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (02): 55-63.doi: 10.12052/gdutxb.210110

• 综合研究 • 上一篇    下一篇

具有执行器故障的非线性系统指定性能控制

邱俊豪, 程志键, 林国怀, 任鸿儒, 鲁仁全   

  1. 广东工业大学 自动化学院, 广东 广州 510006
  • 收稿日期:2021-07-22 出版日期:2023-03-25 发布日期:2023-04-07
  • 通信作者: 程志键(1994-),男,博士研究生,主要研究方向为状态估计、智能电网、网络控制系统,E-mail:chengzhijian2019@163.com
  • 作者简介:邱俊豪(1996-),男,硕士研究生,主要研究方向为非线性系统、多智能体系统、自适应控制
  • 基金资助:
    鹏城实验室重大攻关项目(PCL2021A09)；国家自然科学基金资助项目(62033003，62121004，62003093，62141606)；广东特支计划本土创新创业团队项目(2019BT02X353)；广东省重点领域研发计划项目(2021B0101410005)；广州市科技计划项目(202102020586)

Prescribed Performance Control for a Class of Nonlinear Pure-feedback Systems with Actuator Faults

Qiu Jun-hao, Cheng Zhi-jian, Lin Guo-huai, Ren Hong-ru, Lu Ren-quan   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2021-07-22 Online:2023-03-25 Published:2023-04-07

HTML

PDF (PC)

943

摘要： 针对一类带有全状态约束和执行器故障的非线性系统，提出一种具有指定性能的自适应神经网络输出反馈容错控制方案。首先，建立状态观测器估计系统中的不可测状态，利用径向基神经网络(RBF NNs) 逼近系统中的未知非线性函数。其次，引入非线性映射将状态约束系统转化为一个没有约束的新系统。然后，采用新的性能函数，不仅能使跟踪误差在预先设定的时间内收敛，还可以利用设计参数改变误差的收敛速度。最后证明，该控制方法能够保证闭环系统中所有信号都是半全局一致最终有界的，并通过一个数值仿真验证该方法的有效性。

关键词: 自适应神经网络控制, 执行器故障, 全状态约束, 指定性能, 输出反馈, 动态面控制

Abstract: In this paper, considering a class of nonlinear systems with full state constraints and actuator faults, an adaptive neural network output feedback fault-tolerant control algorithm with prescribed performance is proposed. A state observer is constructed to solve the unmeasurable states problem. Unknown nonlinear functions in the systems are approximated by radial basis function neural networks (RBF NNs) . By introducing the nonlinear mapping, the systems with state constraints are transformed into novel systems without state constraints. Moreover, a novel performance function is utilized to guarantee that the tracking error converges within a preset time. Meanwhile, the convergence speed can be adjusted through the parameter design. Finally, it is proved that the control algorithm ensures that all signals in the closed-loop systems are semi-globally uniformly ultimately bounded. The effectiveness of the algorithm is verified by a numerical simulation.

Key words: adaptive neural networks control, actuator faults, full state constraints, prescribed performance, output feedback, dynamic surface control

中图分类号: 

  • TP13
[1] LI H Y, WU Y, CHEN M. Adaptive fault-tolerant tracking control fordiscrete-time multiagent systems via reinforcement learning algorithm [J]. IEEE Transactions on Cybernetics, 2021, 51(3): 1163-1174.
[2] 田为刚, 王银河, 李玉姣. 一类非线性不确定系统的输出跟踪控制[J]. 广东工业大学学报, 2015, 32(1): 91-97.
TIAN W G, WANG Y H, LI Y J. Fuzzy adaptive output tracking control for a class of uncertain nonlinear systems [J]. Journal of Guangdong University of Technology, 2015, 32(1): 91-97.
[3] LIN G H, LI H Y, MA H, et al. Human-in-the-loop consensus control for nonlinear multi-agent systems with actuator faults [J]. IEEE/CAA Journal of Automatica Sinica, 2022, 9(1): 111-122.
[4] MA H, LI H Y, LIANG H J, et al. Adaptive fuzzy event-triggeredcontrol for stochastic nonlinear systems with full state constraints andactuator faults [J]. IEEE Transactions on Fuzzy Systems, 2019, 27(11): 2242-2254.
[5] 郑晓宏, 董国伟, 周琪, 等. 带有输出约束条件的随机多智能体系统容错控制[J]. 控制理论与应用, 2020, 37(5): 961-968.
ZHENG X H, DONG G W, ZHOU Q, et al. Fault-tolerantcontrol for stochastic multi-agent systems with output constraints [J]. Control Theory & Applications, 2020, 37(5): 961-968.
[6] REN H R, KARIMI H R, LU R Q, et al. Synchronization of network systems via aperiodic sampled-data control with constant delay andapplication to unmanned ground vehicles [J]. IEEE Transactions on Industrial Electronics, 2020, 67(6): 4980-4990.
[7] 周琪, 陈广登, 鲁仁全, 等. 基于干扰观测器的输入饱和多智能体系统事件触发控制[J]. 中国科学:信息科学, 2019, 49: 1502-1516.
ZHOU Q, CHEN G D, LU R Q, et al. Disturbance-observer-based event-triggered control for multi-agent systems withinput saturation [J]. Science China Information Sciences, 2019, 49: 1502-1516.
[8] MA H, LI H Y, LU R Q, et al. Adaptive event-triggered control for aclass of nonlinear systems with periodic disturbances [J]. Science ChinaInformation Sciences, 2020, 63(5): 150212.
[9] 曾光, 孙炳达, 梁慧冰. 一种基于神经网络的直接自适应控制器[J]. 广东工业大学学报, 2000, 17(1): 20-23.
ZENG G, SUN B D, LIANG H B. A direct adaptive controller based on neural network [J]. Journal of Guangdong University of Technology, 2000, 17(1): 20-23.
[10] ZHOU Q, ZHAO S Y, LI H Y, et al. Adaptive neural network tracking control for robotic manipulators with dead zone [J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(12): 3611-3620.
[11] LI Y M, TONG S C. Adaptive neural networks prescribed performance control design for switched interconnected uncertain nonlinear systems [J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(7): 3059-3068.
[12] REN B B, GE S S, TEE K P, et al. Adaptive neural control for outputfeedback nonlinear systems using a barrier Lyapunov function [J]. IEEE Transactions on Neural Networks, 2010, 21(8): 1339-1345.
[13] ZHANG J X, YANG G H. Prescribed performance fault-tolerant control of uncertain nonlinear systems with unknown control directions [J]. IEEE Transactions on Automatic Control, 2017, 62(12): 6529-6535.
[14] WANG C C, YANG G H. Observer-based adaptive prescribed performance tracking control for nonlinear systems with unknown controldirection and input saturation [J]. Neurocomputing, 2018, 284: 17-26.
[15] KOSTARIGKA A K, ROVITHAKIS G A. Adaptive dynamic output feedback neural network control of uncertain MIMO nonlinearsystems with prescribed performance [J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(1): 138-149.
[16] NI J K, AHN C K, LIU L, et al. Prescribed performance fixed-time recurrent neural network control for uncertain nonlinear systems [J]. Neurocomputing, 2019, 363: 351-365.
[17] 杨彬, 周琪, 曹亮, 等. 具有指定性能和全状态约束的多智能体系统事件触发控制[J]. 自动化学报, 2019, 45(8): 1527-1535.
YANG B, ZHOU Q, CAO L, et al. Event-triggered control formulti-agent systems with prescribed performance and full state constraints [J]. Acta Automatica Sinica, 2019, 45(8): 1527-1535.
[18] 赵广磊, 高儒帅, 陈健楠. 具有执行器故障的四旋翼无人机自适应预定性能控制[J]. 控制与决策, 2021, 36(9): 2103-2112.
ZHAO G L, GAO R S, CHEN J N. Adaptive prescribedperformance control of quadrotor with unknown actuator fault [J]. Control and Decision, 2021, 36(9): 2103-2112.
[19] WANG F, CHEN B, LIN C, et al. Adaptive neural network finite-time output feedback control of quantized nonlinear systems [J]. IEEE Transactions on Cybernetics, 2018, 48(6): 1839-1848.
[20] WANG H H, CHEN B, LIN C, et al. Observer-based adaptive neuralcontrol for a class of nonlinear pure-feedback systems [J]. Neurocomputing, 2016, 171: 1517-1523.
[21] ZHOU Q, SHI P, XU S Y, et al. Observer-based adaptive neural network control for nonlinear stochastic systems with time delay [J]. IEEE Transactionson Neural Networks and Learning Systems, 2013, 24(1): 71-80.
[22] YANG H J, SHI P, ZHAO X D, et al. Adaptive output-feedback neuraltracking control for a class of nonstrict-feedback nonlinear systems [J]. Information Sciences, 2016, 334: 205-218.
[23] AREFIM M, ZAREI J, KARIMI H R. Adaptiveoutput feedback neural network control of uncertain non-affine systems with unknowncontrol direction [J]. Journal of the Franklin Institute, 2014, 351: 4302-4316.
[24] 毛骏, 张天平, 夏晓南, 等. 执行器有故障的多输入单输出系统的自适应输出反馈控制[J]. 控制理论与应用, 2016, 33(4): 512-522.
MAO J, ZHANG T P, XIA X N, et al. Adaptive outputfeedback control for multi-input single-output systems with actuatorfailures [J]. Control Theory & Applications, 2016, 33(4): 512-522.
[25] LI Y M, TONG S C. Adaptive neural networks decentralized FTC design for nonstrict-feedback nonlinear interconnected large-scale systems against actuator faults [J]. IEEE Transactions on Neural Networksand Learning Systems, 2017, 28(11): 2541-2554.
[26] LI D P, LIU Y J, TONG S C, et al. Neural networks-based adaptivecontrol for nonlinear state constrained systems with input delay [J]. IEEE Transactions on Cybernetics, 2019, 49(4): 1249-1258.
[27] BOULKROUNE A. A fuzzy adaptive control approach for nonlinear systems with unknown control gain sign [J]. Neurocomputing, 2016, 179: 318-325.
[28] ZHANG T P, XIA M Z, YI Y. Adaptive neural dynamic surface control of strict-feedback nonlinear systems with full state constraintsand unmodeled dynamics [J]. Automatica, 2017, 81: 232-239.
[29] LIU C G, WANG H Q, LIU X P, et al. Adaptive finite-time fuzzy funnel control for nonaffine nonlinear systems [J]. IEEE Transactions on Systems, Man, And Cybernetics:Systems, 2021, 51(5): 2894-2903.
[1] 张林闯, 杜欣烨, 金洪洪, 周伟, 孙永辉. 时变发射概率影响下模态受限线性跳变系统非同步控制[J]. 广东工业大学学报, 2022, 39(05): 46-51,60.
[2] 罗亮； 金朝永； 陈德银； 胡南辉； . 一类非线性不确定时滞系统的输出反馈[J]. 广东工业大学学报, 2008, 25(1): 20-23.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发