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基于事件触发的离散 MIMO 系统自适应评判容错控制

王敏 黄龙旺 杨辰光

王敏, 黄龙旺, 杨辰光. 基于事件触发的离散 MIMO 系统自适应评判容错控制. 自动化学报, 2021, 45(x): 1−12 doi: 10.16383/j.aas.c200721
引用本文: 王敏, 黄龙旺, 杨辰光. 基于事件触发的离散 MIMO 系统自适应评判容错控制. 自动化学报, 2021, 45(x): 1−12 doi: 10.16383/j.aas.c200721
Wang Min, Huang Long-Wang, Yang Chen-Guang. Event-triggered Adaptive Critic Fault-tolerant Control for a Class of Discrete-time MIMO Systems. Acta Automatica Sinica, 2021, 45(x): 1−12 doi: 10.16383/j.aas.c200721
Citation: Wang Min, Huang Long-Wang, Yang Chen-Guang. Event-triggered Adaptive Critic Fault-tolerant Control for a Class of Discrete-time MIMO Systems. Acta Automatica Sinica, 2021, 45(x): 1−12 doi: 10.16383/j.aas.c200721

基于事件触发的离散 MIMO 系统自适应评判容错控制

doi: 10.16383/j.aas.c200721
基金项目: 国家自然科学基金(61773169, U20A20200, 61973129), 广东省自然科学基金项目(2019B151502058), 广东省重点领域研发计划项目(2020B1111010002), 广东海洋经济发展专项(粤自然资合[2020]018号), 佛山市科技创新项目(2018IT100322)资助
详细信息
    作者简介:

    王敏:华南理工大学自动化科学与工程学院教授. 主要研究方向为智能控制与学习、机器人控制、网络控制系统. E-mail: auwangmin@scut.edu.cn

    黄龙旺:华南理工大学自动化科学与工程学院博士研究生. 主要研究方向为智能控制和网络控制系统. E-mail: longwang_huang@126.com

    杨辰光:华南理工大学自动化科学与工程学院教授. 主要研究方向为人机交互和智能系统设计. E-mail: cyang@ieee.org

Event-triggered Adaptive Critic Fault-tolerant Control for a Class of Discrete-time MIMO Systems

Funds: Supported by the National Natural Science Foundation of China (61773169, U20A20200, 61973129), the Guangdong Natural Science Foundation (2019B151502058), the Key-Area Research and Development Program of Guangdong Province (2020B1111010002), the Guangdong Marine Economic Development Project (2020018); and the Foshan Science and Technology Innovation Team Special Project (2018IT100322)
  • 摘要: 本文针对具有执行器故障的一类离散非线性多输入多输出(Multi-input multi-output, MIMO)系统, 提出了一种基于事件触发的自适应评判容错控制方案. 该控制方案包括评价和执行网络. 在评价网络里, 为了缓解现有的非光滑二值效用函数可能引起的执行网络跳变问题, 利用高斯函数构建了一个光滑的效用函数, 并采用评价网络近似最优性能指标函数. 在执行网络里, 通过变量替换将系统状态的将来信息转化成关于系统当前状态的函数, 并结合事件触发机制设计了最优跟踪控制器. 该控制器引入了动态补偿项, 不仅能够抑制执行器故障对系统性能的影响, 而且能够改善系统的控制性能. 稳定性分析表明所有信号最终一致有界且跟踪误差收敛于原点的有界小领域内. 数值系统和实际系统的仿真结果验证了该方案的有效性.
  • 图  1  网络控制系统框图

    Fig.  1  Schematic diagram of networked control systems

    图  2  子系统1输出跟踪效果

    Fig.  2  Output tracking performance of subsystem 1

    图  3  子系统2输出跟踪效果

    Fig.  3  Output tracking performance of subsystem 2

    图  4  事件触发间隔

    Fig.  4  Event triggering interval

    图  5  长期性能函数

    Fig.  5  Long-term performance function

    图  6  执行网络的权值范数

    Fig.  6  Norm of action NN weights

    图  7  评价网络的权值范数

    Fig.  7  Norm of critic NN weights

    图  8  例二关节1的输出跟踪效果

    Fig.  8  Tracking performance of joint 1 of Example 2

    图  9  例二关节2的输出跟踪效果

    Fig.  9  Tracking performance of joint 2 of Example 2

    图  10  例二的事件触发间隔

    Fig.  10  Event triggering interval of Example 2

    图  11  例二的长期性能指标函数

    Fig.  11  Long-term performance function of Example 2

    图  12  例二的执行网络权值范数

    Fig.  12  Norm of action NN weights of Example 2

    图  13  例二的评价网络权值范数

    Fig.  13  Norm of critic NN weights of Example 2

    表  1  仿真实验对比一

    Table  1  Comparison of simulation results

    触发次数 MAE ABO (bit/s)
    无执行器补偿,非光滑效用函数 921 $ z_{1,1} $ 0.0201 5894.4
    $ z_{2,1} $ 0.0335
    有执行器补偿,非光滑效用函数 907 $ z_{1,1} $ 0.0185 5804.8
    $ z_{2,1} $ 0.0229
    有执行器补偿,光滑效用函数 843 $ z_{1,1} $ 0.0130 5395.2
    $ z_{2,1} $ 0.0147
    注: “无执行器补偿” 表示$ \omega_j(k) = \hat{{{W}}}^{\rm T}_j(k)\varphi({{Z}}_j(k_t)) $; “有执行器补偿” 表示$ \omega_j(k) = \hat{{{W}}}^{\rm T}_j(k)\varphi({{Z}}_j(k_t))+\hat{\mu}_j(k) $; “非光滑效用函数” 表示若$ |z_{j,1}(k)| $大于一个给定的正常数, 则$ q_j(k) = 1 $. 否则, $ q_j(k) = 0 $; “光滑效用函数” 表示$ q_j(k) = 1 - e^{-z_{j, 1}^{2}(k)/\eta_j} $.
    下载: 导出CSV

    表  2  仿真实验对比二

    Table  2  Comparison of simulation results

    触发条件 触发次数 MAE ABO (bit/s) CPU耗时(s)
    SETC 843 $ z_{1,1} $ 0.0130 5395.2 0.6875
    $ z_{2,1} $ 0.0147
    DETC 801 $ z_{1,1} $ 0.0129 5126.4 0.8125
    $ z_{2,1} $ 0.0144
    下载: 导出CSV
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  • 收稿日期:  2020-09-04
  • 录用日期:  2021-01-15
  • 网络出版日期:  2021-02-07

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