Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Fault tolerant control for nonlinear systems using sliding mode and adaptive neural network estimator

Qi, H, Shi, Y, Li, S, Tian, Y, Yu, DL and Gomm, JB (2019) Fault tolerant control for nonlinear systems using sliding mode and adaptive neural network estimator. Soft Computing. ISSN 1432-7643

[img]
Preview
Text
Fault tolerant control for nonlinear systems using sliding mode and adaptive neural network estimator.pdf - Published Version
Available under License Creative Commons Attribution.

Download (489kB) | Preview

Abstract

This paper proposes a new fault tolerant control scheme for a class of nonlinear systems including robotic systems and aeronautical systems. In this method, a sliding mode control is applied to maintain system stability under the post-fault dynamics. A neural network is used as on-line estimator to reconstruct the change rate of the fault and compensate for the impact of the fault on the system performance. The control law and the neural network learning algorithms are derived using the Lyapunov method, so that the neural estimator is guaranteed to converge to the fault change rate, while the entire closed-loop system stability and tracking control is guaranteed. Compared with the existing methods, the proposed method achieved fault tolerant control for time-varying fault, rather than just constant fault. This greatly expands the industrial applications of the developed method to enhance system reliability. The main contribution and novelty of the developed method is that the system stability is guaranteed and the fault estimation is also guaranteed for convergence when the system subject to a time-varying fault. A simulation example is used to demonstrate the design procedure and the effectiveness of the method. The simulation results demonstrated that the post-fault is stable and the performance is maintained.

Item Type: Article
Uncontrolled Keywords: 0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Electronics & Electrical Engineering (merged with Engineering 10 Aug 20)
Publisher: Springer
Related URLs:
Date Deposited: 26 Feb 2020 10:52
Last Modified: 26 Feb 2020 10:52
DOI or Identification number: 10.1007/s00500-019-04618-8
URI: http://researchonline.ljmu.ac.uk/id/eprint/12085

Actions (login required)

View Item View Item