Abstract
Based on the control structure of a Field Oriented Control (FOC) Permanent Magnet Synchronous Motor (PMSM) modified for the purpose of using a Linear Quadratic Regulator (LQR) to achieve superior control performance by minimizing a performance criterion, this article presents the improvement of the performances of this type of controller by using computational intelligence algorithms: Simulated Annealing (SA) algorithm, Grey Wolf Optimization (GWO) algorithm, and Reinforcement Learning - Twin Delayed Deep Deterministic Policy Gradient (RL-TD3) agent algorithm. It presents the numerical simulations performed in the Matlab/Simulink development environment and the comparative results, emphasizing the superiority of the results obtained by using LQR-type controllers optimized by computational intelligence algorithms.
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