Optimized Sensorless Control of Five-Phase Permanent Magnet Synchronous Motor Using a Genetic Algorithm-Real Time Implementation

This paper presents an optimized backstepping control approach that combines a genetic algorithm (GA) and a robust sliding model observer (SMO) for achieving high-performance sensorless control of a 5-phase permanent magnet synchronous motor (5P-PMSM). Initially, a robust nonlinear strategy based on backstepping control is introduced to accurately track desired reference values of speed and direct-axis current. The stability analysis of the overall control system employs the Lyapunov theorem to ensure the convergence of tracking errors. However, the arbitrary selection of gains in backstepping control can impact controller quality. To address this, a novel numerical technique leveraging a genetic algorithm is proposed. This technique aims to determine optimal control gains while adhering to the physical limitations of the 5P-PMSM drive. Moreover, for sensor reduction purposes, a sliding model observer is devised to extract electromotive force information from the 5P-PMSM drive. Meanwhile, the angular position and motor speed are estimated using an adaptive back EMF observer. To validate the proposed solution, real-time modeling was implemented on an FPGA using the OPAL-RT 4500 simulator. The paper concludes by presenting real-time simulation results, illustrating the efficacy of the proposed approach across diverse test trajectories.