Adaptive and intelligent control of permanent magnet synchronous motor (PMSM) using a combination of fuzzy logic and gray wolf algorithm under fault condition

Nowadays, permanent magnet synchronous motors have been widely used in industry due to the elimination of excitation losses, longer life and higher efficiency. Errors in engine and drive systems are unavoidable during operation. Therefore, a suitable scenario should be considered for when these systems fail. If the necessary predictions and control algorithms are not considered for the error conditions, heavy damages will be imposed on equipment and devices, especially at high power levels. One of the common control methods in inverters and synchronous motor drives is the proportional-integral control method. In most cases, simple proportional-integral control with fixed interest rates is used. In this paper, fuzzy logic with suitable membership functions is used to adaptively and intelligently determine the benefits of proportional-integral controller to control the speed of the permanent magnet synchronous motor in the event of an error. By combining fuzzy logic and gray wolf algorithm to achieve the optimal controller, a method is presented that takes advantage of the fuzzy controller along with the proportional-integral controller and using the gray wolf algorithm for optimization. Reconstruction results in optimal and stable control under various fault conditions. The proportional-integral controller interests used in conjunction with the gray wolf algorithm for speed control are determined using fuzzy logic for error conditions. In this paper, the proposed method in MATLAB software Simulink environment is simulated in two stages, once for step changes of reference speed and torque and once under three types of errors, and the results show improved reference speed tracking and reduction Distortion of three-phase currents and torque fluctuations.