Fuzzy Adaptive Fixed-Time Tracking Control for State-Constrained Nonlinear Systems with Application to Induction Motors

The unknown functions, state constraints, and input saturation are widespread in practical systems because of physical property and specified performance. This paper primarily addresses the fixed-time tracking control issue for uncertain nonstrict-feedback nonlinear systems (NSFNSs) with state constraints and input saturation. Firstly, the fuzzy logic system (FLS) is utilized. It is used to approximate the unknown function. Secondly, a new auxiliary dynamical system is constructed to deal with the input saturation. Thirdly, an innovative barrier Lyapunov function (BLF) is designed to realize time-varying asymmetric state constraints. Then, by combining the fuzzy adaptive control technique and an innovative fixed-time stability theory, a novel backstepping fuzzy adaptive fixed-time tracking control scheme is proposed. The proposed tracking control approach ensures that the tracking errors converge to a small region around zero in fixed time, and all the state variables abide by their corresponding constraints. Compared with existing results, the presented method not only solves the inherent singularity issue in fixed-time control, but also obtains a tighter estimation on the settling time (ST). The validity and superiority of the presented method are demonstrated by the simulation and experimental studies of an induction motor system.