Adaptive fuzzy command filtered backstepping control for uncertain pure-feedback systems. (October 2022)

• Record Type: Journal Article
• Authors: Chen, Lian; Wang, Qing; Hu, ChangHua
• Is Part Of: ISA transactions. Volume 129(2022)Part B
• Page Start: 204
• Page End: 213
• Publication Date: 2022-10
• Subjects: Adaptive fuzzy control -- Command filtered backstepping control -- Pure-feedback systems; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.08.048
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This paper considers tracking control of non-affine pure-feedback systems with uncertain disturbances; a new adaptive fuzzy command filtered backstepping controller is presented. First, the non-affine difficulty of pure-feedback systems is solved with the help of the mean-value theorem, and the fuzzy logic systems (FLSs) are adopted to estimate the system uncertainties which contain external disturbances. Next, a novel command-filtered technology is proposed to avoid the so-called "explosion of complexity" in the backstepping design process. The compensation mechanism is employed to eliminate the shortcoming of the traditional dynamic surface control. The presented control scheme ensures that all closed-loop signals are bounded, and the system output can track the given reference signal. The main contribution of this paper is that the developed approach is not only generalized to uncertain non-affine pure-feedback systems, which extends the practical applications of classical command filtered schemes, but also has an error compensation system, which improves tracking performance in comparison with existing dynamic surface control proposals. Finally, several simulation experiments validate the effectiveness and superiority of the proposed control scheme. Highlights: A fuzzy adaptive command filtered controller is first generalized to pure-feedback systems with uncertain external disturbances. The running efficiency simulation shows that the proposed control algorithm reduces computational complexity by about 59% compared to the DSC algorithm.