This article introduces a rapid and stable actor-critic learning-based Takagi–Sugeno–Kang type-2 fuzzy controller (AL-TFC) designed for nonlinear and uncertain applications. The critic-based adaptive tuning network of the AL-TFC employs the backpropagation of the error method, utilizing a neural network to implement the critic network. Parameter updates are performed using Lyapunov criteria to mitigate issues associated with local minima. The AL-TFC serves as a high-precision tracker suitable for diverse applications affected by environmental disturbances and parametric uncertainties. To assess the AL-TFC’s performance, simulations of the proposed system are conducted using MATLAB/Simulink under various scenarios, including the no-load condition, abrupt load-torque shifts, uncertainty in the system’s parameters, abrupt interruption of the phase, and the influence of noise. The efficacy of the AL-TFC is assessed through a comparative study with two benchmark controllers. In contrast to the Fuzzy Logic Controlled Variable Reluctance Stepper Motor (FLC-VRS) and Speed Control of Hybrid Stepper Motor, which includes uncertainties (SCSM), the proposed AL-TFC reduces tracking errors by approximately 10.63% and 12.75%, respectively, during incremental load changes. The simulation results indicate that the proposed strategy delivers precise and adaptable speed responses, making it well-suited for applications characterized by nonlinearity and uncertainty.
S et al. (Fri,) studied this question.