RBFPI_STASMRC Control Strategy for SPIM Drives in Electric Vehicle Applications

Electric vehicle (EV) drive systems demand control strategies with fast torque response, strong disturbance rejection, and reliable real-time operation; however, conventional schemes often degrade under dynamic operating conditions. To address these limitations, this paper applies an intelligent adaptive hybrid control structure to the field-oriented control (FOC) of a six-phase induction motor (SPIM) for EV traction drive applications. The main research contribution of this work is twofold. First, an adaptive RBF neural-network-tuned PI controller in the outer speed loop is integrated with a Super-Twisting Sliding Mode controller augmented by a plug-in Repetitive Controller (STSM-RC) in the inner current loop, enabling mitigation of inter-loop coupling effects, harmonic current components, and robustness degradation under uncertainties. Second, the proposed hybrid control architecture is systematically applied and evaluated in an EV-oriented context, providing performance insights under dynamic loading and real-time execution constraints not explicitly addressed in prior SPIM control studies. The STSM-RC current controller ensures finite-time convergence, robust current regulation, and effective suppression of chattering and periodic disturbances, enabling smooth torque production. Meanwhile, the adaptive RBF-PI speed controller identifies SPIM nonlinear dynamics online and automatically adjusts control gains to maintain accurate speed tracking under varying load conditions. Comparative results demonstrate improved dynamic response, enhanced robustness, and reduced steady-state errors compared with conventional control schemes. The effectiveness of the proposed approach is validated through MATLAB/Simulink simulations and real-time simulations executed on a CPU-based OPAL-RT OP5707 platform, with system responses monitored using an external oscilloscope.