Adaptive Neural Network Fixed-Time Control Design for Bilateral Teleoperation With Time Delay

• Published in: IEEE transactions on cybernetics Vol. 52; no. 9; pp. 9756 - 9769
• Main Authors: Zhang, Shuang, Yuan, Shuo, Yu, Xinbo, Kong, Linghuan, Li, Qing, Li, Guang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive systems; Algorithms; Control theory; Delay effects; Delays; Environment models; Fixed-time control; Network control; Neural networks; Nonlinear systems; Radial basis function; Robots; teleoperation; Teleoperators; time delay; Time lag; Time-varying systems; uncertainties; Uncertainty; Upper bounds
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2021.3063729

In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for a class of nonlinear bilateral teleoperation systems. First, an adaptive control scheme is applied to estimate the upper bound of delay, which can resolve the predicament that delay has significant impacts on the stability of bilateral teleoperation systems. Then, radial basis function neural networks (RBFNNs) are utilized for estimating uncertainties in bilateral teleoperation systems, including dynamics, operator, and environmental models. Novel adaptation laws are introduced to address systems' uncertainties in the fixed-time convergence settings. Next, a novel adaptive fixed-time neural network control scheme is proposed. Based on the Lyapunov stability theory, the bilateral teleoperation systems are proved to be stable in fixed time. Finally, simulations and experiments are presented to verify the validity of the control algorithm.