Control and performance analyses of a DC motor using optimized PIDs and fuzzy logic controller

Based on the no-free-lunch theorem, researchers have been proposing optimization algorithms for solving complex engineering problems. This paper analyzes the performance of five metaheuristics: Equilibrium Optimizer (EO), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Differential Evolution (DE), and Genetic Algorithm (GA) in fine-tuning the gains of a Proportional-Integral-Derivative (PID) to control the speed of a DC motor. The selected metaheuristics, in addition to being from distinct classes, are well established in their respective groups. The methods and findings of this study can be summarized in three phases. First, the mathematical model of the DC motor is deduced. Second, detailed descriptions of the aforementioned algorithms are presented. Furthermore, the structures of the applied controllers are discussed. Third, comparisons based on statistical indicators and analyses in the time and frequency domains, in addition to robustness and load disturbance tests, are performed. The results revealed that if a sufficient number of runs is given for each metaheuristic, despite being in different runs, all algorithms are able to propose the same optimal gain values. TLBO presented the highest speed, while GA and DE were the slowest in finding optimal values. Additionally, the results were compared with the Opposition-Based Learning Henry Gas Solubility Optimization (OBL/HBO)-based PID, reported to have better results than some previously published works on this topic, and a Fuzzy Logic Controller (FLC). The five optimized controllers obtained approximately the same results and outperformed the OBL/HGO-based PID, but the FLC was superior compared to the metaheuristic-based PIDs.