ADRC Parameter Tuning of Permanent Magnet Synchronous Motor Based on Improved CPO Algorithm

At present, the control algorithm of permanent magnet synchronous motor(PMSM) is still dominated by the traditional proportional-integral-differential(PID) control mode, which has some problems such as easy to be affected by external disturbance, rapidity and irreconcilable overshoot. A second-order nonlinear active disturbance rejection controller(ADRC) is designed to replace the traditional PID controller, and a position control system based on vector control is constructed. Aiming at the problems that the ADRC involves many parameters, the tuning process depends heavily on the working experience of engineers, the process is complicated, and the debugging is complicated, an improved optimization algorithm is proposed to realize the parameter tuning of ADRC. Firstly, the good points set is used to optimize the individual location distribution of the population to make the population distribution more uniform and increase the global search ability and stability in the early stage. Secondly, the nonlinear dynamic weights are introduced to better balance the global search and local search. Finally, the adaptive t-distribution perturbation and variation are introduced to increase the development ability and avoid falling into the local optimal solution. Through simulation experiments, particle swarm optimization algorithm(PSO) algorithm, gray wolf optimization(GWO) algorithm and original crested porcupine optimization(CPO) algorithm are compared, and the results show that the proposed method performs better.