Adaptive model-based dynamic event-triggered output feedback control of a robotic manipulator with disturbance

作     者：Gao, Jie Kang, Erlong He, Wei Qiao, Hong 

作者机构：Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Key Lab Res & Applicat Robot Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn 320 Yue Yang Rd Shanghai 200031 Peoples R China 

出 版 物：《ISA TRANSACTIONS》 (美国仪表、系统和自动控制协会会刊)

年 卷 期：2022年第122卷

页      面：63-78页

核心收录：

学科分类：08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 0811[工学-控制科学与工程] 081102[工学-检测技术与自动化装置] 

基　　金：National Key Research and Development Program of China [2017YFB1300200, 2017YFB1300203] National Natural Science Foundation of China [91648205, 61627808, 91948303] Strategic Priority Research Program of Chinese Academy of Sci-ence [XD-B32050100] 

主　　题：Adaptive neural network control Event-triggered mechanism Robotic manipulator Model-based control State observer 

摘      要：This paper focuses on the stable tracking control of the manipulator with constrained communication, unmeasurable velocity, and nonlinear uncertainties. An NN observer-depended output feedback scheme in the discrete-time domain is developed by virtue of the model-based dynamic eventtriggered backstepping technique in the channel of sensor to controller. For generalizing the zeroorder-holder (ZOH) implementation, a plant model is built to approximate the triggered states in the time flow, and according to which, the control law is fabricated. Based on model-based error events, we construct a dead-zone triggered condition with a dynamically adjustable threshold, making the threshold evolve with the system performance, to achieve flexible communication scheduling and avoid the accumulation of triggers in small tracking errors. The internal and external nonlinear uncertainties are online compensated by the neural network, and the aperiodic adaptive law is derived in the sense of control stability to save the computation. Finally, the conditions for semi-global ultimate uniform bounded (SGUUB) of all variables are given via impulse Lyapunov analysis, and a positive lower bound in the time interval between consecutive executions to guarantee the Zeno free behavior is obtained. Simulations are conducted on a three-link manipulator to illustrate the effectiveness of our method. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.