Enhanced Robust Control of Induction Motor Using Combined Optimal Model Predictive Control With Super-Twisting Algorithm - REZGUI, S.-E.; NEMOUCHI, B. (Advances in Electrical and Computer Engineering)
This paper presents a novel strategy for induction motor control that combines Optimal Model Predictive Control (OMPC) with the Super-Twisting Algorithm (STA) to enhance the performance of field-oriented control (IFOC) strategy under disturbances and uncertainties. OMPC is exploited for its capability to optimally handle multivariable systems with constraints, but suffers from high computational demands, sensitivity to model inaccuracies, and limited robustness against disturbances. To address these limitations, the proposed approach integrates STA, a second-order sliding mode technique, which provides robustness when subjected to model mismatch and disturbances, while reducing the chattering effect typically associated with classical sliding mode control. By incorporating OMPC with STA into the speed and currents loops of the IFOC technique, the system gains enhanced robustness and disturbance rejection capabilities, without increasing computational cost making it viable for real-time applications in complex control scenarios. This synergetic approach ensures stable and efficient performance in the face of internal variations (like parameters variation) and external perturbations (variable references and load torque). Simulation results demonstrate that the combined OMPC-STA strategy outperforms traditional PI and SMC methods in terms of tracking accuracy, robustness, providing a more reliable control solution for high-performance drives.