Multi-stage adaptive speed control with torque ripple optimization for a switched reluctance motor in electric vehicle applications

This work addresses the problem of speed control for switched reluctance motors (SRM) used in electric vehicles, with a focus on minimizing torque ripple by optimizing the current reference shape. This issue is formulated as a constrained optimization problem, particularly difficult due to the nonlinear and nonconvex relationship between currents and torque. In this context, a two-stage optimization strategy is proposed. Using the particle swarm optimization (PSO) technique, the first stage computes in offline mode, a first estimate of the optimal parameters of the SRM current reference signal. The second stage refines, in real time, this estimate using the extremum seeking (ES) technique. The speed controller is designed with the backstepping approach based on a SRM nonlinear model taking into account the magnetic saturation phenomenon of this machine. This ensures good control performance across a wide operating range. The effectiveness of the proposed control strategy is demonstrated through simulations conducted in MATLAB-Simulink. Results indicate that the proposed strategy achieves a 48% reduction in torque ripple compared to a non-adaptive controller and a 12% reduction compared to a strategy based on single-stage optimization performed in offline mode.