Abstract
In order to improve the efficiency of human–robot interaction (HRI), it is necessary to carry out research on precise control of the manipulator. In this paper, an adaptive non-singular fast terminal sliding mode control scheme is proposed for robot manipulators to solve the trajectory tracking problem with model uncertainty and external disturbances. At first, a novel non-singular fast terminal sliding mode surface is proposed, and by introducing an auxiliary function, the singularity problem caused by the inverse of the error-related matrix could be avoided in the controller design process. Then, the controller is developed by using Lyapunov synthesis. A robust adaptive strategy is used to deal with lumped uncertainty, with an adaptive update law designed to compensate for the upper bound of lumped uncertainty whose upper bound is prior unknown. Finally, a two-link robot manipulators as a simulation example is given to illustrate the effectiveness of the proposed scheme. Compared with other similar algorithms, the proposed adaptive non-singular fast terminal sliding mode control scheme has higher efficiency and smaller computational complexity for the reason that no piecewise continuous function is needed to be constructed during the controller design.
Funder
National Natural Science Foundation of China
National Natural Science Foundation of Zhejiang Province
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference22 articles.
1. Decentralized control of robot manipulators: Nonlinear and adaptive approaches;Liu;IEEE Trans. Autom. Control.,1999
2. On manipulator control by exact linearization;Kreutz;IEEE Trans. Autom. Control.,1989
3. Stability of PID control for industrial robot arms;Rocco;IEEE Trans. Robot. Autom.,1996
4. Adaptive iterative learning control for robot manipulators;Tayebi;Automatica,2004
5. Adaptive finite-time bounded-H∞ tracking control for a class of manipulator system;Li;Control. Theory Appl.,2021