Sensorless Control Strategy of Permanent Magnet Synchronous Motor Based on Fuzzy Sliding Mode Observer

Abstract

In this paper, a sensorless control strategy of permanent magnet synchronous motor (PMSM) based on fuzzy sliding mode observer (FSMO) is proposed. On the premise of satisfying the Lyapunov stability condition, a sliding mode observer (SMO) is constructed. The sigmoid function is used instead of the sign function as the switching function. The parameters of sigmoid function are adjusted in real time by establishing fuzzy rules to change the convergence characteristics of sigmoid function, so as to improve the observation performance. The back EMF signal extracted by the SMO can be made smoother by using the back EMF adaptive law, which reduces the chattering of the system and the observation error. In order to solve the problem that the traditional phase locked loop (PLL) can not be used when the positive and negative speed of the motor is switched, a tangent function PLL is proposed in this paper. Through the use of tangent function, the value and symbol of back EMF are avoided from entering the system, so as to realize the accurate estimation of rotor speed and rotor position under the condition of positive and negative speed switching. The designed Fuzzy Sliding Mode Observer is simulated in Matlab/Simulink, and the experimental verification is carried out on the 2kW surface mounted PMSM vector control platform with TMS320F28335 as the main control chip. Simulation and experimental results show that this method can effectively track the changes of rotor speed and position when the motor is switched between positive and negative speed, and has the characteristics of fast convergence, small chattering and good robustness.INDEX TERMS Permanent magnet synchronous motor, sensorless control, fuzzy sliding mode observer, back EMF adaptive law, tangent function phase locked loop.

High-order Supper-twisting Based Terminal Sliding Mode Control Applied on Three Phases Permanent Synchronous Machine

“…Other articles suggested some new super-twisting sliding mode based on intelligent proportional-integral control [36], adaptive control based on a super-twisting sliding mode [37], sliding mode control based on the fractionalorder method [38,39], sliding mode based on intelligent control [40,41], and hybrid control composed of adaptive, sliding mode and intelligent control [42,43]. However, optimizing more performance characteristics of PMSM and applying robust control to achieve high-performance, robust to uncertainties, and external disturbances remain the researchers' main challenge.…”