Trajectory tracking based on adaptive fast non-singular terminal sliding mode control
by Jiqing Chen; Xu Liu; Chaoyang Zhao; Rongxian Mo; Chunlin Huang; Ganwei Cai
International Journal of Computing Science and Mathematics (IJCSM), Vol. 16, No. 1, 2022

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.

Online publication date: Mon, 07-Nov-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computing Science and Mathematics (IJCSM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com