Adaptive observer-based fault detection and active tolerant control for unmanned aerial vehicles attitude system. Issue 24 (2019)

• Record Type: Journal Article
• Authors: Lijia, Cao; Yu, Tang; Guo, Zhang
• Is Part Of: IFAC-PapersOnLine. Volume 52:Issue 24(2019)
• Page Start: 47
• Page End: 52
• Publication Date: 2019
• Subjects: unmanned aerial vehicle -- adaptive observer -- radial basis function neural network -- fault-detection -- fault-tolerant control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2019.12.379
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: A robust adaptive observer combined with radial basis function neural network (RBFNN) is designed for the unmanned aerial vehicles (UAVs) fault-detection system is proposed in this paper. Firstly, the fault dynamics model with unknown disturbance of the unmanned aerial vehicle's attitude system is established, and a robust adaptive observer combined with radial basis function neural network is designed for the vehicle's fault-detection, then, the detected fault combined with a robust controller is applied to design the fault-tolerant controller. In the end, the stability and effective of the fault detection and tolerant system is proved by Lyapunov theory and simulation.