Torque ripple minimization and speed control of switched reluctance motor employing model predictive controller

Switched Reluctance Motor (SRM) drives have gained a lot of attention in the drive domain because of their built-in benefits, which include ease of use, longevity, and compatibility with high-speed applications. Precise speed control is essential to minimize torque variations and maximize SRM performance. Traditionally, the proportional–integral controller is used for speed control. The nonlinearity and uncertain complexities of SRM have shifted the focus to sophisticated control techniques from conventional. In this work, the Model Predictive Controller (MPC) is applied for SRM speed control, and its performance is compared with that of conventional control methods. MPC improves speed control precision and disturbance rejection by taking into account input restrictions and system dynamics through optimization across a constrained prediction horizon, a strong fault tolerance capability. Furthermore, three different models are developed to control the speed of the SRM using a PI controller, a lookup table-based controller, and an adaptive neuro-fuzzy inference system controller to compare the results of MPC. Through extensive simulations, the study seeks to show how successful MPC is in terms of speed tracking accuracy, transient responsiveness, and stability when compared to standard control techniques, especially under variable operating circumstances and load disturbances. The practicality of the suggested control approach is illustrated by comparing it with other conventional control methods, and its performances are evaluated by using real-time simulator OPAL-RT 4510 under various situations.