带有执行器失效的确定时间事件触发机制

doi:10.3969/j.issn.1674-1951.2021.10.009

华电技术 ›› 2021, Vol. 43 ›› Issue (10): 73-79.doi: 10.3969/j.issn.1674-1951.2021.10.009

带有执行器失效的确定时间事件触发机制

黄堃锋(), 洪嘉纯, 吴宇深, 朱厚耀^*()

广州大学机械与电气工程学院,广州 510006

收稿日期:2021-06-02 修回日期:2021-07-26 出版日期:2021-10-25 发布日期:2021-10-11
通讯作者: 朱厚耀
作者简介:黄堃锋（2000—）,女,广东台山人,从事智能制造、机器人控制等方面的研究（E-mail： kunfenghuang@163.com）。
基金资助:
广东省教育厅人才项目(2018KQNCX197);广东省自然科学基金项目(2019A1515110995);广州市科技计划项目(202002030286);广州大学校内项目(YG2020009);广州大学省级大学生创新训练项目(S202111078041);2021 年广东省科技创新战略专项资金(“攀登计划”专项资金)资助立项项目(pdjh2021b0396)

Event-triggered mechanism with settling time for actuator failures

HUANG Kunfeng(), HONG Jiachun, WU Yushen, ZHU Houyao^*()

School of Mechanical and Electrical Engineering,Guangzhou University,Guangzhou 510006,China

Received:2021-06-02 Revised:2021-07-26 Online:2021-10-25 Published:2021-10-11
Contact: ZHU Houyao

摘要/Abstract

摘要：

在工业系统的实际操作过程中可能会发生未知形式的执行器故障,导致控制系统不稳定甚至崩溃。针对具有执行器故障的不确定非线性系统,提出了一种有关事件触发的规定建立时间共识自适应补偿控制方法。设计了一种有限时间神经网络自适应控制器,引入非线性变换函数以确保跟踪误差在规定的建立时间内收敛到预定义的时间间隔内。提出了一种利用反步技术建立的事件触发式设定时间稳定补偿控制策略,在该策略下系统不仅可以在发生故障时实现快速补偿,而且可以预设有限的收敛时间。仿真试验表明,该策略能够有效节省网络带宽,保证系统的全局一致有界收敛。

关键词: 控制系统, 执行器失效, 神经网络系统, 规定的稳定时间, 事件触发控制

Abstract:

Unknown forms of actuator failures during the operation of industrial systems might lead to the instability or even crash of the control systems. For a class of uncertain nonlinear systems with actuator failures,an event-triggered adaptive compensation control with prescribed settling time consensus is proposed.A finite time neural network adaptive controller which introduces a nonlinear transformation function is designed,so as to ensure the tracking error converges to the predefined interval within a prescribed settling time. Meanwhile,based on the backstepping technology,an event-triggered compensation control strategy with prescribed settling time consensus is proposed. Under this strategy,the system can achieve rapid compensation for the event of failure and preset the finite convergence time. Finally,the simulation experiments validate that the proposed strategy is able to effectively save network bandwidth and ensure the global uniform bounded convergence of the system.

Key words: control system, actuator failure, neutral network system, prescribed settling time, event-triggered control

中图分类号:

TP273

黄堃锋, 洪嘉纯, 吴宇深, 朱厚耀. 带有执行器失效的确定时间事件触发机制[J]. 华电技术, 2021, 43(10): 73-79.

HUANG Kunfeng, HONG Jiachun, WU Yushen, ZHU Houyao. Event-triggered mechanism with settling time for actuator failures[J]. Huadian Technology, 2021, 43(10): 73-79.

图/表 6

图1

图2

图3

图4

图5

表1

触发量数据

T/s	0—2	2—4	4 $—$ 6	6 $—$ 8	8 $—$ 10	10 $—$ 12	12 $—$ 14
触发次数	64	58	51	42	42	28	34

表1

参考文献 22

[1]	CUI Q Q, DONG W, CHEN Z X. Research on the networked control system technology applied in intelligent building[C]// 2010 8th World Congress on Intelligent Control and Automation, 2010:4307-4311.
[2]	CHEN C, LING L, ZHU S, et al. On-demand transmission for edge-assisted remote control in industrial network systems[J]. IEEE Transactions on Industrial Informatics, 2020, 16(7): 4842-4854. doi: 10.1109/TII.9424
[3]	FOURATI F, ALOUINI M S. Artificial intelligence for satellite communication: A review [EB/OL].(2021-01-01)[2021-05-25]. https://www.researchgate.net/publication/348802867_Artificial_Intelligence_for_Satellite_Communication_A_Review.
[4]	FUNABIKI N, TAKEFUJI Y, LEE K C. A neural network model for traffic controls in multistage interconnection networks[C]// International Joint Conference on Neural Networks. IEEE, 1992.
[5]	WANG Z, SUN Y, LIANG B. Synchronization control for bilateral teleoperation system with position error constraints: A fixed-time approach[J]. ISA Transactions, 2019, 93: 125-136. doi: 10.1016/j.isatra.2019.03.003
[6]	SONG Y, WANG Y, HOLLOWAY J, et al. Time-varying feedback for regulation of normal-form nonlinear systems in prescribed finite time[J]. Automatica, 2017, 83: 243-251. doi: 10.1016/j.automatica.2017.06.008
[7]	SUN J, YANG J, LI S, et al. Sampled-data-based event-triggered active disturbance rejection control for disturbed systems in networked environment[J]. IEEE Transactions on Cybernetics, 2019, 49: 556-566. doi: 10.1109/TCYB.2017.2780625
[8]	CHEN W, WANG J, MA K, et al. Adaptive event-triggered neural control for nonlinear uncertain system with input constraint[J]. International Journal of Robust and Nonlinear Control, 2020, 30(10): 3801-3815. doi: 10.1002/rnc.v30.10
[9]	MAITY D, BARAS J S. Event based control of stochastic linear systems[C]// International Conference on Event-based Control, 2015.
[10]	LU H, HU Y, GUO C, et al. Cluster synchronization for a class of complex dynamical network system with randomly occurring coupling delays via an improved event-triggered pinning control approach[J]. Journal of the Franklin Institute, 2019, 357(4): 2167-2184. doi: 10.1016/j.jfranklin.2019.11.076
[11]	QIU A B, AL-DABBAGH A W, CHEN T W. A tradeoff approach for optimal event-triggered fault detection[J]. IEEE Transactions on Industrial Electronics, 2019, 66(3): 2111-2121. doi: 10.1109/TIE.41
[12]	JING Y H, YANG G H. Adaptive fuzzy output feedback fault-tolerant compensation for uncertain nonlinear systems with infinite number of time-varying actuator failures and full-state constraints[J]. IEEE Transactions on Cybernetics, 2019:1-11.
[13]	MA H, LI H, LIANG H, et al. Adaptive fuzzy event-triggered control for stochastic nonlinear systems with full state constraints and actuator faults[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(11): 2242-2254. doi: 10.1109/TFUZZ.91
[14]	WU L B, PARK J H, XIE X P, et al. Neural network adaptive tracking control of uncertain MIMO nonlinear systems with output constraints and event-triggered inputs[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(2): 695-707. doi: 10.1109/TNNLS.5962385
[15]	THEODORAKOPOULOS A, ROVITHAKIS G A. Guaranteeing preselected tracking quality for uncertain strict-feedback systems with deadzone input nonlinearity and disturbances via low-complexity control[J]. Automatica, 2015, 54: 135-145. doi: 10.1016/j.automatica.2015.01.038
[16]	XIA J, ZHANG J, SUN W, et al. Finite-time adaptive fuzzy control for nonlinear systems with full state constraints[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(7): 1541-1548. doi: 10.1109/TSMC.6221021
[17]	HUANG X Q, LIN W, YANG B. Global finite-time stabilization of a class of uncertain nonlinear systems[J]. Automatica, 2005, 41(5): 881-888. doi: 10.1016/j.automatica.2004.11.036
[18]	WANG F, CHEN B, SUN Y M, et al. Finite-time fuzzy control of stochastic nonlinear systems[J]. IEEE Transactions on Cybernetics, 2020, 50(6): 2617-2626. doi: 10.1109/TCYB.6221036
[19]	WANG N, HAO F. Event-triggered sliding mode control with adaptive neural networks for uncertain nonlinear systems[J]. Neurocomputing, 2021, 436: 184-197. doi: 10.1016/j.neucom.2021.01.055
[20]	LIU X Y, HO D W C, XIE C L. Prespecified-time cluster synchronization of complex networks via a smooth control approach[J]. IEEE Transactions on Cybernetics, 2020, 50(4): 1771-1775. doi: 10.1109/TCYB.6221036
[21]	ROMANYUK V A, SOVA O Y. The hierarchical control system model of wireless sensor networks using unmanned aerial vehicles[C]// 2015 IEEE International Conference Actual Problems of Unmanned Aerial Vehicles Developments (APUAVD), 2015:239-242.
[22]	ZHAO K, SONG Y, MA T, et al. Prescribed performance control of uncertain euler-lagrange systems subject to full-state constraints[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 29(8): 3478-3489. doi: 10.1109/TNNLS.2017.2727223

带有执行器失效的确定时间事件触发机制

Event-triggered mechanism with settling time for actuator failures

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 22

相关文章 15

编辑推荐

Metrics

本文评价

[1]	陈逸珲, 林令淇, 田鑫, 张栋梁, 吴军, 刘子晨. 三级式风电AVC协调控制策略[J]. 综合智慧能源, 2022, 44(4): 20-27.
[2]	刘文慧, 严博文, 吴江, 任一君, 孔维政, 谌际宇. 基于平行控制理论的循环流化床锅炉床温智能预测模型[J]. 综合智慧能源, 2022, 44(3): 50-57.
[3]	秦海山, 栾丛超, 侯晓宁, 种道彤. 单抽供热机组负荷控制系统的解耦设计及动态响应特性研究[J]. 华电技术, 2021, 43(3): 40-47.
[4]	宫月, 李秋香, 张心语, 龚钢军, 金璐. 能源工业网络空间安全风险与模型研究[J]. 华电技术, 2021, 43(2): 40-45.
[5]	刘文彬. 电力企业工控PLC系统立体综合防控体系的设计与实践[J]. 华电技术, 2021, 43(2): 46-52.
[6]	王建, 王天屹, 翟亚红, 蒋天伦. IEC 62443 系统安全要求与等级保护基本要求对比研究[J]. 华电技术, 2021, 43(2): 72-76.
[7]	姜寅，任荭葳，陈凯，朱长江. 基于BP神经网络的风电系统控制器I/O硬件故障自诊断方法[J]. 华电技术, 2020, 42(5): 55-60.
[8]	陈明恩，汪贤浩. 一种基于云集中控制的电力瘦SCADA系统实现方法[J]. 华电技术, 2020, 42(2): 7-11.
[9]	马亮，付冉. 火电厂脱硫分散控制系统信号干扰故障分析[J]. 华电技术, 2019, 41(9): 53-55.
[10]	胡美玲1，赵雪瑞2，赵耀丽2. 基于以太网的水电站卷扬式启闭机控制系统的设计[J]. 华电技术, 2019, 41(6): 13-16.
[11]	张松奇，杨勇，许涛. 基于DCS的布袋除尘器脉冲阀顺序控制改造分析[J]. 华电技术, 2019, 41(2): 76-77.
[12]	张云斌，柴海超，王欣荷. 提高火力发电厂自动电压控制系统投入率的措施[J]. 华电技术, 2019, 41(1): 55-58.
[13]	李文秀. 600MW机组DEH冗余VP卡升级改造简析[J]. 华电技术, 2018, 40(8): 57-58.
[14]	谢小鹏1，林玥廷2，林英明2. 火电厂设备状态监测与故障预警的研究[J]. 华电技术, 2018, 40(6): 7-9.
[15]	陈春林，袁世通. 某机组非停原因分析及循环水控制系统优化[J]. 华电技术, 2018, 40(6): 44-46.