Abstract
Several bio-inspired meta-heuristic algorithms have been developed for solving real-world optimization problems, which mimic the social behavior of animals in nature. For control engineers, designing and tuning an optimal controller for a power converter has always been a challenge. This article presents a comparative analysis of the application of different bio-inspired meta-heuristic optimization techniques for designing a controller for a dc-dc boost converter circuit. This article has illustrated the development of a linear quadratic regulator (LQR) control framework with five different bio-inspired optimization algorithms, i.e., particle swarm optimization, genetic algorithm, grey wolf optimization(GWO), dragonfly algorithm, and whale optimization. GWO coupled with LQR has outperformed the other four optimization methods with 0\% overshoot, 0.48\% steady-state error, and the least execution time of 5.17 seconds. Hence, GWO has been proposed for power converter controller optimization due to its fast and efficient optimization performance. The controller design and optimization have been carried out in the Matlab/Simulink environment.
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