Decentralized finite-time adaptive fault-tolerant synchronization tracking control for multiple UAVs with prescribed performance. Issue 16 (November 2020)

• Record Type: Journal Article
• Authors: Yu, Ziquan; Zhang, Youmin; Liu, Zhixiang; Qu, Yaohong; Su, Chun-Yi; Jiang, Bin
• Is Part Of: Journal of the Franklin Institute. Volume 357:Issue 16(2020)
• Page Start: 11830
• Page End: 11862
• Publication Date: 2020-11
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2019.11.056
• Languages: English
• ISSNs: 0016-0032

Abstract

Highlights: A decentralized fault-tolerant control scheme is developed for multiple UAVs against actuator faults. Finite-time synchronization tracking control is achieved by utilizing the fractional power of the synchronized errors. The prescribed performance function is used to constrain the synchronization tracking performance. The norms of the weighting vectors are used for the estimation to reduce the computational burden. Abstract: This paper is concerned with the decentralized finite-time fault-tolerant attitude synchronization tracking control problem for multiple unmanned aerial vehicles (multi-UAVs) with prescribed performance. Failure to counteract actuator faults in the formation flight of multi-UAVs in a limited time may lead to catastrophic consequences. By integrating the prescribed performance functions into the synchronization tracking errors, a new set of errors is defined. Based on the transformed errors, a finite-time attitude synchronization tracking control scheme is developed by using neural networks and finite-time differentiator techniques. The neural networks are utilized to identify the unknown nonlinear terms induced by uncertainties and actuator faults. To reduce the computational burden caused by estimating the weight vectors, the norms of weight vectors are used for the estimation, such that the number of adaptive parameters is significantly reduced and independent from the number of neurons. The finite-time differentiators are utilized to estimate the intermediate control signals and their derivatives. Moreover, auxiliary dynamic signals with explicit consideration of differentiator estimation errors are introduced into the control scheme to guarantee the finite-time convergences of the synchronized tracking errors. Furthermore, it is shown that by using the Lyapunov method, all UAVs can track their individual attitude references, while the synchronized tracking errors among UAVs are all bounded in finite time and confined within the prescribed performance bounds. Finally, comparative simulation studies on multi-UAVs are conducted to verify the effectiveness of the proposed scheme.