Title: Trajectory tracking based on adaptive fast non-singular terminal sliding mode control

Authors: Jiqing Chen; Xu Liu; Chaoyang Zhao; Rongxian Mo; Chunlin Huang; Ganwei Cai

Addresses: School of Mechatronic Engineering, Guangxi University, Nanning, Guangxi, China ' School of Mechatronic Engineering, Guangxi University, Nanning, Guangxi, China ' School of Mechatronic Engineering, Guangxi University, Nanning, Guangxi, China ' School of Mechatronic Engineering, Guangxi University, Nanning, Guangxi, China ' Nanning ZhengTeng Agricultural Machinery Co., Ltd, Nanning, Guangxi, 530004, China ' School of Mechatronic Engineering, Guangxi University, Nanning, Guangxi, 530004, China

Abstract: Aiming at the binocular vision system with dynamic errors and external disturbances, a trajectory tracking control method based on the adaptive fast non-singular terminal sliding mode is proposed. Firstly, the fast non-singular terminal sliding mode is adopted to ensure that the system converges in a finite time while avoiding singularity; Secondly, the adaptive radial basis function neural network (RBFNN) is used to approximate the dynamics errors, friction and external disturbance to eliminate the influence of uncertain factors; Thirdly, the particle swarm optimisation (PSO) algorithm is used to optimise the parameters to avoid the mapping failure caused by unreasonable parameter setting; Finally, the Lyapunov function is established according to the Lyapunov stability, and the theoretical stability of the system is proved. The experimental results show that the proposed trajectory tracking control method can effectively improve the convergence speed and tracking accuracy, enhance the robustness and weaken the chattering of the system.

Keywords: trajectory tracking; terminal sliding mode control; radial basis function neural network; particle swarm optimisation algorithm.

DOI: 10.1504/IJCSM.2022.126787

International Journal of Computing Science and Mathematics, 2022 Vol.16 No.1, pp.24 - 34

Received: 14 Jun 2021
Accepted: 26 Feb 2022
Published online: 07 Nov 2022 *

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