Design and Implementation of Fuzzy-PI Controllers for PMSM Based on Multi-Objective Optimization Algorithms

Various intelligent algorithms have been applied to our daily lives, such as fuzzy theory, neural networks, and machine learning. These methods are widely used for solving many real-world problems; however, these algorithms also exhibit deficiencies and limitations. This paper introduces the recently improved algorithm, known as multi-objective particle swarm optimization, based on decomposition and dominance (D^2 MOPSO) in order to design the permanent magnet synchronous motor (PMSM) fuzzy controller for different objects. This means that the user can easily change the customized controller, according to their requirements. Furthermore, this paper compares the final decision of the controller parameter with other algorithms: The multiobjective particle swarm optimization with crowding distance (MOPSO-CD), and nondominated sorting genetic algorithm II (NSGA-II). The simulation results of the three algorithms indicate the optimum PMSM controller parameter in the computing software MATLAB. Finally, we implement the fuzzy controller in an embedded system (DSP28069) to demonstrate that our design matches the reality system response and meets the user's demands with ease.