Fixed-time adaptive neural tracking control for a class of uncertain nonstrict nonlinear systems - Ba, Desheng; Li, Yuan-Xin; Tong, Shaocheng (Neurocomputing (Amsterdam), 2019)
This paper addresses the fixed-time adaptive neural control of nonstrict feedback nonlinear system. With the help of neural networks and the backstepping technical, a fixed-time adaptive neural control scheme is presented. To guarantee closed-loop stability, a new semiglobal practical fixed-time stability (SPFTS) criterion is set up. Based on the established SPFTS criterion, we can show that both the tracking performance and the closed-loop stability can be preserved in a fixed time via the presented approach. Compared with the existing finite-time control, the convergence time of the propose fixed-time control scheme does not rely on the initial states. Finally, the proposed technique is demonstrated with simulation results.