Indirect neural-enhanced integral sliding mode control for finite-time fault-tolerant attitude tracking of spacecraft

Yao, Qijia, Jahanshahi, Hadi, Bekiros, Stelios, Mihalache, Sanda Florentina and Alotaibi, Naif D. (2022) Indirect neural-enhanced integral sliding mode control for finite-time fault-tolerant attitude tracking of spacecraft. Mathematics, 10 (14). ISSN 2227-7390

Text (mathematics-10-02467-v2) - Published Version Available under License Creative Commons Attribution. Download (5MB)

Abstract

In this article, a neural integral sliding mode control strategy is presented for the finite-time fault-tolerant attitude tracking of rigid spacecraft subject to unknown inertia and disturbances. First, an integral sliding mode controller was developed by originally constructing a novel integral sliding mode surface to avoid the singularity problem. Then, the neural network (NN) was embedded into the integral sliding mode controller to compensate the lumped uncertainty and replace the robust switching term. In this way, the chattering phenomenon was significantly suppressed. Particularly, the mechanism of indirect neural approximation was introduced through inequality relaxation. Benefiting from this design, only a single learning parameter was required to be adjusted online, and the computation burden of the proposed controller was extremely reduced. The stability argument showed that the proposed controller could guarantee that the attitude and angular velocity tracking errors were regulated to the minor residual sets around zero in a finite time. It was noteworthy that the proposed controller was not only strongly robust against unknown inertia and disturbances, but also highly insensitive to actuator faults. Finally, the effectiveness and advantages of the proposed control strategy were validated using simulations and comparisons.

Item Type:	Article
Official URL:	https://www.mdpi.com/journal/mathematics
Additional Information:	© 2022 The Authors
Divisions:	LSE Health
Subjects:	Q Science > QA Mathematics
Date Deposited:	01 Sep 2022 14:39
Last Modified:	16 Nov 2024 21:12
URI:	http://eprints.lse.ac.uk/id/eprint/116420

Actions (login required)

View Item