Low-speed sensorless control of permanent magnet synchronous motors via the Cauchy-Gauss hybrid mutation particle swarm optimization algorithm

In the process of tracking the speed and rotor position of a permanent magnet synchronous motor, the conventional proportional-integral (PI) control method has difficulty satisfying the requirements of the control system. Therefore, an alternative based on particle swarm optimization is proposed in this article. To improve the optimization performance, a nonlinear differential decreasing inertia weight and an asynchronous time-varying learning factor are adopted. A Cauchy-Gauss hybrid mutation strategy is introduced to prevent the algorithm from falling easily into a local optimum. Then, the high-frequency injection method is used to estimate the rotor speed and position. Simulations and experiments show that the proposed Cauchy-Gauss hybrid mutation particle swarm optimization method has good dynamic performance and anti-interference capability and can satisfy the requirements of the system.