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

Jie Gao; Erlong Kang; Wei He; Hong Qiao

doi:10.1016/j.isatra.2021.04.023

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

ISA Trans. 2022 Mar:122:63-78. doi: 10.1016/j.isatra.2021.04.023. Epub 2021 Apr 23.

Authors

Jie Gao¹, Erlong Kang¹, Wei He², Hong Qiao³

Affiliations

¹ The State Key Lab of Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Beijing Key Laboratory of Research and Application for Robotic Intelligence of "Hand-Eye-Brain" Interaction, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
² The School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
³ The State Key Lab of Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; CAS Center for Excellence in Brain Science and Intelligence Technology, 320 Yue Yang Road, Shanghai, 200031, China. Electronic address: hong.qiao@ia.ac.cn.

PMID: 33965203
DOI: 10.1016/j.isatra.2021.04.023

Abstract

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 event-triggered backstepping technique in the channel of sensor to controller. For generalizing the zero-order-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.

Keywords: Adaptive neural network control; Event-triggered mechanism; Model-based control; Robotic manipulator; State observer.