Robust Adaptive Neural Network Tracking Control With Optimized Super-Twisting Sliding-Mode Technique for Induction Motor Drive System

Abstract

This article proposes a robust adaptive supertwisting sliding-mode controller (STSMC) for high-precision IM drive. First, the STSMC is developed for controlling and stabilizing the IM. However, the control performance may be influenced due to exterior disorders along with parameter variations since the IM model is not accurately known during operation. Therefore, it is essential to estimate the lumped disturbance to strength the robustness of the STSMC using system model-based disturbance estimator. Second, to optimize the control performance, a robust adaptive neural-network tracking controller is developed to evade the constraints on knowing the disorders and uncertainties upper bounds. The RANTC combines an STSMC and a recurrent radial basis function network (RRBFN) estimators. The RRBFNs are employed to approximate the IM dynamics along with estimating the optimum super-twisting control algorithms online. The RRBFN training is developed via adaptive particle swarm optimization (APSO) algorithm to attain enhanced performance in less learning iterations, which is the critical issue in real-time applications. Third, to imitate the STSMC algorithms for preserving the robust control characteristics without the necessity of the IM parameters, a direct adaptive intelligent controller (DAIC) based on Takagi–Sugeno–Kang-type recurrent Petri fuzzy-neural-network is developed. Furthermore, the online adaptive laws are derived according to Lyapunov theory, so that the system stability can be assured. The validity of control schemes is verified by simulation and experimentation. The simulation and experimental results confirm the superiority of the STSMC-SMDE, RANTC, and DAIC schemes compared with STSMC irrespective of exterior disorders along with indefinite model uncertainties.