Data-driven-based event-triggered tracking control for non-linear systems with unknown disturbance

被引:25
作者
Li, Hai-Feng [1 ]
Wang, Ying-Chun [1 ]
Zhang, Hua-Guang [1 ]
机构
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China
基金
中国国家自然科学基金;
关键词
closed loop systems; nonlinear control systems; control system synthesis; linear systems; tracking; observers; adaptive control; learning systems; data-driven-based event-triggered; nonlinear systems; unknown disturbance; event-triggered model-free adaptive tracking control problem; unknown bounded disturbance; general dynamic linearisation model framework; disturbance input; event-triggered-based model-free adaptive control algorithm; input; output measurement data; disturbance estimator; event-triggering mechanism; observer-based controller; disturbance compensation; tracking error; system output; FREE ADAPTIVE-CONTROL; DESIGN;
D O I
10.1049/iet-cta.2019.0051
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A novel event-triggered model-free adaptive tracking control problem is studied for non-linear systems with unknown bounded disturbance. A general dynamic linearisation model framework with disturbance input is developed and event-triggered-based model-free adaptive control algorithm is designed by using pseudo-partial derivatives method and input/output measurement data. Owing to the existence of unknown disturbance, a disturbance estimator is designed based on the optimisation criterion technique. Then, a new event-triggering mechanism with dead-zone operator is designed to improve the utility of network communication resources without Zeno phenomenon. Then, an observer-based controller with disturbance compensation is developed, such that the tracking error between the system output and desired output signal converges to a small residual set around the origin. Finally, two simulation examples are provided to show the effectiveness and practicability of the proposed approach.
引用
收藏
页码:2197 / 2206
页数:10
相关论文
共 31 条
[1]  
[Anonymous], 1994, THESIS
[2]   Data-Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control [J].
Bu, Xuhui ;
Hou, Zhongsheng ;
Zhang, Hongwei .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 29 (05) :1514-1524
[3]  
Bu XH, 2012, CONTROL ENG APPL INF, V14, P42
[4]   Constrained data-driven optimal iterative learning control [J].
Chi, Ronghu ;
Liu, Xiaohe ;
Zhang, Ruikun ;
Hou, Zhongsheng ;
Huang, Biao .
JOURNAL OF PROCESS CONTROL, 2017, 55 :10-29
[5]   Event-based security control for discrete-time stochastic systems [J].
Ding, Derui ;
Wang, Zidong ;
Wei, Guoliang ;
Alsaadi, Fuad E. .
IET CONTROL THEORY AND APPLICATIONS, 2016, 10 (15) :1808-1815
[6]   USE OF HAMMERSTEIN MODELS IN IDENTIFICATION OF NONLINEAR-SYSTEMS [J].
ESKINAT, E ;
JOHNSON, SH ;
LUYBEN, WL .
AICHE JOURNAL, 1991, 37 (02) :255-268
[7]   A new delay system approach to network-based control [J].
Gao, Huijun ;
Chen, Tongwen ;
Lam, James .
AUTOMATICA, 2008, 44 (01) :39-52
[8]   Online adaptive optimal control for continuous-time Markov jump linear systems using a novel policy iteration algorithm [J].
He, Shuping ;
Song, Jun ;
Ding, Zhengtao ;
Liu, Fei .
IET CONTROL THEORY AND APPLICATIONS, 2015, 9 (10) :1536-1543
[9]   An Overview of Dynamic-Linearization-Based Data-Driven Control and Applications [J].
Hou, Zhongsheng ;
Chi, Ronghu ;
Gao, Huijun .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017, 64 (05) :4076-4090
[10]   Controller-Dynamic-Linearization-Based Model Free Adaptive Control for Discrete-Time Nonlinear Systems [J].
Hou, Zhongsheng ;
Zhu, Yuanming .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2013, 9 (04) :2301-2309