Improving Single-Phase Induction Motor Speed Control Using Model Reference Adaptive System and Fuzzy-Pid Regulator

As industries strive to enhance their applications to meet growing market demand, accurate speed control of single-phase induction motors (SPIMs) remains a crucial concern. This paper presents the modelling and simulation of SPIM speed control based on a hybrid Model Reference Adaptive System (MRAS) integrated with a Fuzzy–PID regulator. Superior performance was achieved by integrating MRAS with a fuzzy-PID regulator using an adaptive self-tuning mechanism. The purpose of this integration was to leverage the adaptive nature of MRAS and the robustness of the Fuzzy-PID regulator to enhance performance and reliability in SPIM drives without the need for physical sensors. The SPIM speed was modelled using differential equations representing both electrical and mechanical dynamics. MRAS was implemented using motor voltage equations, with an adaptive model estimating the rotor speed. The Fuzzy-PID regulator optimized control performance by processing the error and its rate of change through a fuzzy controller, with the output fed into a PID controller to ensure error stabilization. A review of relevant literature on SPIM and associated control theories was conducted, and several journal papers were analyzed. Simulation of the proposed MRAS–Fuzzy-PID approach in MATLAB demonstrated that sensorless speed regulation considerably reduced rise time, enabling the motor to reach the desired speed quickly while eliminating steady-state error compared to systems without controllers. The results indicate that the rise time was reduced by 65.5%, the overshoot decreased by 58.9%, the steady-state error decreased by 71.8%, and the Integral of Absolute Error (IAE) was minimized. These improvements ensured stable operation under varying load conditions, with minimal fluctuations in speed. Integrating MRAS with a Fuzzy-PID controller further enhances the speed stability, robustness, and adaptability of SPIM drives.