Optimal-Parameters-Based Model Predictive Position Control of Planar Switched Reluctance Motors

In this article, an adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm is proposed to optimize the parameters of the model predictive position control (MPPC) algorithm for planar switched reluctance motors (PSRMs). Based on the self-designed PSRM in our lab, the dynamic model of discrete state space equation is established, and then the prediction model is established and the cost function is defined, the optimal control sequence is calculated. In addition, the ASAPSO algorithm was used to optimize the parameters of the MPPC. The algorithm adopted the nonlinear dynamic inertia weight coefficient to balance the global search ability and local improvement ability of particles. The hyperbolic tangent function is used to control the acceleration coefficient and balance the self-cognition ability of particles. Simulated annealing operator is introduced to increase the ability of particles to jump out of local optimum. Finally, the optimal parameters obtained by the ASAPSO algorithm are used for simulation verification, and compared with the traversal method. The simulation results verify the effectiveness of the proposed MPPC based on optimal parameters.