基于状态观测器的网络控制系统的自愈控制

doi:10.3901/JME.2023.18.095

机械工程学报 ›› 2023, Vol. 59 ›› Issue (18): 95-110.doi: 10.3901/JME.2023.18.095

• 特邀专栏：人工自愈与装备自主健康 • 上一篇下一篇

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基于状态观测器的网络控制系统的自愈控制

王忻^1,2, 尹逊和¹, 梁华清¹, 陈丽红³

1. 北京交通大学电子信息工程学院北京 100044;
2. 北京交通大学威海校区实验中心威海 264400;
3. 中国机械工程学会北京 100048

收稿日期:2022-11-05 修回日期:2023-06-16 出版日期:2023-09-20 发布日期:2023-12-07
通讯作者: 尹逊和(通信作者),男,1966年出生,博士,教授,博士研究生导师。主要研究方向为网络控制系统的理论与应用、信息物理系统、认知控制、自愈控制、智能电网中的通信与控制等。E-mail:xhyin@bjtu.edu.cn
作者简介:王忻,女,1996年出生。主要研究方向为自愈控制及其在网络控制系统中的应用。E-mail:18120262@bjtu.edu.cn;梁华清,女,1988 年出生,博士研究生。主要研究方向为自愈控制,网络控制系统的理论与应用。E-mail:hqliang@bjtu.edu.cn;陈丽红,女,1966年出生,高级工程师。主要研究方向为控制理论应用。E-mail:chenlh@cmes.org

Self-healing Control Based on State Observer for Networked Control Systems

WANG Xin^1,2, YIN Xunhe¹, LIANG Huaqing¹, CHEN Lihong³

1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044;
2. Laboratory Centre, Weihai Campus, Beijing Jiaotong University, Weihai 264400;
3. Chinese Mechanical Engineering Society, Beijing 100048

Received:2022-11-05 Revised:2023-06-16 Online:2023-09-20 Published:2023-12-07

摘要/Abstract

摘要： 提出基于状态观测器的自愈控制方法。为了对网络控制系统中发生的执行器故障/传感器故障进行检测和诊断，改进基于状态观测器的故障诊断方法，该方法利用Lyapunov稳定性理论和线性矩阵不等式法获得观测增益矩阵和故障估计增益参数；同时，为了改善执行器故障估计过程中的延时的问题，设计执行器故障估计辅助信号。其次，为了提高故障检测的实时性，改进故障检测策略；通过对执行器故障估计和传感器故障估计的试验数据的分析，设计最优故障定位方法，解决对网络控制系统中的故障进行快速定位的问题。再次，为了降低故障对网络控制系统性能的影响，设计故障诊断与基于控制律重构相结合的自愈控制方法。最后，在搭建的网络控制系统中对提出的自愈控制方法进行仿真验证。

关键词: 自愈控制, 网络控制系统, 执行器故障, 传感器故障, 故障诊断

Abstract: The self-healing control based on state observer is presented. A fault diagnosis algorithm based on state observer is improved in order to detect and diagnose the actuator/sensor faults in networked control systems(NCSs). In this algorithm, the observation gain matrix and fault estimation gain matrix is obtained by using Lyapunov stability theory and linear matrix inequality. Meanwhile, an auxiliary signal for actuator fault estimation is designed for improving delay of estimation of actuator fault. Then, a fault detection strategy is improved for making the real-time performance of fault detection better. By analyzing the experimental data of actuator and sensor fault estimation, an optimal fault location method is designed to deal with the problem of fast fault location in NCSs. Moreover, self-healing control combining fault diagnosis and reconfiguration based on control law is provided aims to reduce the influences of faults on the performance of NCSs. Finally, the self-healing control proposed in this paper is verified with simulation in network environment.

Key words: self-healing control, networked control systems, actuator fault, sensor faults, fault diagnose

中图分类号:

TP273

王忻, 尹逊和, 梁华清, 陈丽红. 基于状态观测器的网络控制系统的自愈控制[J]. 机械工程学报, 2023, 59(18): 95-110.

WANG Xin, YIN Xunhe, LIANG Huaqing, CHEN Lihong. Self-healing Control Based on State Observer for Networked Control Systems[J]. Journal of Mechanical Engineering, 2023, 59(18): 95-110.

参考文献

[1] 尹逊和,王忻. 自愈控制系统研究的综述、分析与展望[J]. 控制理论与应用,2021,38(8):1145-1158. YIN Xunhe,WANG Xin. Review,analysis and prospect of self-healing control system[J]. Control Theory & Applications,2021,38(8):1145-1158.
[2] 秦红霞,谭志海,葛亮,等. 智能配电网自愈控制系统技术研究与设计[J]. 电力系统保护与控制,2014,42(22):134-139. QIN Hongxia,TAN Zhihai,GE Liang,et al. Study and design of smart distribution grid self-healing control system technology[J]. Power System Protection and Control,2014,42(22):134-139.
[3] XIA Dong,YU Haiping. Design and application of typical schemes for cooperative feeder self-healing control in distribution network[C]//2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA),Dalian,China. NewYork:IEEE,2021:115-119.
[4] DONG Qihuan,DONG Jiaojia,ZHU Lin,et al. Impact of self-healing control on reliability evaluation in distribution system with microgrid[C]//2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe),Espoo,Finland. New York:IEEE,2021:1-5.
[5] 张安龙,李艳,黄福全,等. 基于动态拓扑分析的配电网自适应保护与自愈控制方法[J]. 电力系统保护与控制,2019,47(11):111-117. ZHANG Anlong,LI Yan,HUANG Fuquan,et al. Adaptive protection and self-healing control method of distribution network based ondynamic topology analysis[J]. Power System Protection and Control,2019,47(11):111-117.
[6] 董世丹傑,杨超,姜燕,等. 基于物联网的光伏并网配电网自愈控制方法研究[J]. 电气自动化,2021,43(1):48-50. DONGSHI Danjie,YANG Chao,JIANG Yan,et al. Research on self-healing control method for photovoltaic grid-connected distribution networks based on the internet of things[J]. Electrical Automation,2021,43(1):48-50.
[7] QI Xin,QI Juntong,THEILLIOL D,et al. Self-healing control design under actuator fault occurrence on single-rotor unmanned helicopters[J]. Journal of Intelligent & Robotic Systems,2016,84(1):21-35.
[8] WANG Ang,CHEN Fuyang,GONG Huajun. Self-healing control for attitude system of hypersonic flight vehicle with body flap faults[J]. IEEE Access,2018,6:19121-19130.
[9] LIANG Xiaohui,WANG Qing,HU Changhua,et al. Self-healing control for large launch vehicle based on extended state observer and adaptive dynamic programming[J]. IEEE Access,2020,8:43013-43026.
[10] 夏天. 基于改进粒子群优化的双闭环自愈调控算法研究[D]. 沈阳:辽宁大学,2015. XIA Tian. The research of double close loop self-healing system based on the improved particle swarm optimizer[D]. Shenyang:Liaoning University,2015.
[11] 高金吉. 人工自愈与机器自愈调控系统[J]. 机械工程学报,2018,54(8):83-94. GAO Jinji. Artificial self-recovery and machinery self-recovery regulation system[J]. Journal of Mechanical Engineering,2018,54(8):83-94.
[12] 高金吉. 工业互联网赋能装备智能运维与自主健康[J]. 计算机集成制造系统,2019,25(12):3013-3025. GAO Jinji. Intelligent maintenance and autonomous health of equipments enabled by industrial Internet[J]. Computer Integrated Manufacturing Systems,2019,25(12):3013-3025.
[13] 高金吉. 航空发动机振动故障监控智能化[J]. 测控技术,2019,38(1):1-4. GAO Jinji. Intelligent monitoring of aero-engine vibration failure[J]. Measurement & Control Technology,2019,38(1):1-4.
[14] 高金吉. 人工自愈概论[J]. 机械工程学报,2021,57(2):1-10. GAO Jinji. Overview on artificial self-recovery[J]. Journal of Mechanical Engineering,2021,57(2):1-10.
[15] LIU Zheng,HAN Honggui,YANG Hongyan,et al. Design of broad learning-based self-healing predictive control for sludge bulking in wastewater treatment process[J]. IEEE Transactions on Industrial Informatics,2023,19(4):6220-6233.
[16] 褚菲,鲍文超,傅逸灵,等. 基于贝叶斯网络的重介质选煤过程自愈控制[J]. 控制工程,2022,29(10):1866-1873. CHU Fei,BAO Wenchao,FU Yiling,et al. Research on self-healing control of dense medium coal separation process based on bayesian network[J]. Control Engineering of China,2022,29(10):1866-1873.
[17] 杨旭,高晶晶,高峰,等. 数据驱动的带钢热连轧过程监测与自愈控制研究综述[J]. 冶金自动化,2022,46(6):1-15. YANG Xu,GAO Jingjing,GAO Feng,et al. A review of data-driven process monitoring andself-recovery control for hot strip rolling[J]. Metallurgical Industry Automation,2022,46(6):1-15.
[18] WITCZAK M,ZEGAR D,PAZERA M. Design of an adaptive sensor and actuator fault estimation scheme with a quadratic boundedness approach[C]//Advanced and intelligent Computations in Diagnosis and Control. NewYork:Springer Cham,2016:49-59.
[19] 俞立. 鲁棒控制:线性矩阵不等式处理方法[M]. 北京:清华大学出版社,2002. YU Li. Robust control:A linear matrix inequality approach[M]. Beijing:Tsinghua University Press,2002.
[20] GUO Y,ZHANG Y,SHI P,et al. Multi-model based adaptive reconfiguration control for flight control systems with actuator faults[C]//2011 Chinese Control and Decision Conference (CCDC),May 23-25,2011,Mianyang,China. NewYork:IEEE,2011:1335-1340.
[21] ORTIZ-TORRES G,CASTILLO P,REYES-REYES J. An actuator fault tolerant control for vtol vehicles using fault estimation observers:Practical validation[C]//2018 International Conference on Unmanned Aircraft Systems (ICUAS). New York IEEE,2018:1054-1062.
[22] BLANKE M,KINNAERT M,LUNZE J,et al. Diagnosis and fault-tolerant control[M]. Berlin:Springer,2015.

基于状态观测器的网络控制系统的自愈控制

Self-healing Control Based on State Observer for Networked Control Systems

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