Intelligent Nonsingular Terminal Sliding Mode Controlled Nonlinear Time-Varying System Using RPPFNN-AMF

This article aims to create an intelligent control system to alter the inherent nonlinear and time-varying control characteristics of a nonlinear time-varying system by using an intelligent nonsingular terminal sliding mode control recurrent Petri probabilistic fuzzy neural network (INTSMCRPPFNN) that features an intelligent nonsingular terminal sliding mode control. This article first designs a nonsingular terminal sliding mode control (NTSMC) system to track the states of a nonlinear time-varying system. Creating a working NTSMC for practical applications is quite complex because the detailed system dynamics, which includes the unpredictability of the controlled plant, is not available beforehand. Thus, this study suggests that a recurrent Petri probabilistic fuzzy neural network with asymmetric membership function (RPPFNN-AMF) can act as a close substitute for the ideal NTSMC to resolve its existing complications. Furthermore, this study modifies an adaptive compensator to proactively adjust for the potential calculated deviance of the RPPFNN-AMF. Asymptotical stability is assured by using the Lyapunov stability method, which generates the RPPFNN-AMF's online learning algorithms. Finally, in the case study, some experimental results of a maximum torque per ampere operated interior permanent magnet synchronous motor position servo drive are provided to verify the effective and robust qualities of the suggested INTSMCRPPFNN.