Fixed-time neural network control of a robotic manipulator with input deadzone. (April 2023)

• Record Type: Journal Article
• Authors: Wu, Yifan; Niu, Wenkai; Kong, Linghuan; Yu, Xinbo; He, Wei
• Is Part Of: ISA transactions. Volume 135(2023)
• Page Start: 449
• Page End: 461
• Publication Date: 2023-04
• Subjects: Fixed-time control -- Adaptive neural networks -- Input deadzone -- Uncertainty -- Robotic system; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2022.09.030
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In this paper, a fixed-time control method is proposed for an uncertain robotic system with actuator saturation and constraints that occur a period of time after the system operation. A model-based control and a neural network-based learning approach are proposed under the framework of fixed-time convergence, respectively. We use neural networks to handle the uncertainty, and design an adaptive law driven by approximation errors to compensate the input deadzone. In addition, a new structure of stabilizing function combining with an error shifting function is introduced to demonstrate the robotic system stability and the boundedness of all error signals. It is proved that all the tracking errors converge into the compact sets near zero in fixed-time according to the Lyapunov stability theory. Simulations on a two-joint robot manipulator and experiments on a six-joint robot manipulator verified the effectiveness of the proposed fixed-time control algorithm. Highlights: A new error shifting function is designed for the proposed constraints. A fixed-time control scheme integrates the input deadzone and system uncertainty. Constraints are applied at some time after the start, instead of the beginning. A novel structure of the Lyapunov stabilizing function is proposed.