Fuzzy adaptive super‐twisting algorithm based sliding‐mode observer for sensorless control of permanent magnet synchronous motor

Abstract

Abstract Aiming at the issues of slow convergence, phase delay, and chattering in the sensorless vector control system of permanent magnet synchronous motor (PMSM) controlled by sliding mode observer (SMO), a fuzzy adaptive super‐twisting (ST) SMO sensorless control algorithm is proposed. The fuzzy adaptive algorithm is used to estimate the uncertain boundary and adaptively adjust sliding mode gain, which is used in the ST algorithm to accelerate the convergence speed of sliding mode gain, and eliminate the system delay caused by phase locked loop and phase compensation, and improve the estimation accuracy of speed and rotor position. In this algorithm, the sigmoid function is used instead of the signum function in the traditional SMO to suppress system chattering. Lyapunov stability theorem is used to obtain the stable conditions of position and speed observer at motoring mode. The saturated ST‐SMO algorithm is verified by Matlab/Simulink. The simulation results show that compared with the traditional SMO, the fuzzy ST‐SMO algorithm proposed in this paper has faster convergence speed in the variable speed and the variable load of PMSM sensorless control system, which has significantly reduced chattering, obtained more accurate speed and rotor position and has better dynamic response and robustness.