Design and Optimization of PID Controller based on Metaheuristic algorithms for Hybrid Robots

Abstract

Metaheuristics optimization techniques are significant to search methods that are used to solve challenging Artificial intelligence (AI) problems. In hybrid robot control systems, Meta-heuristic optimization methods are widely applied. The major goal of this paper is to develop optimized PID control parameters to improve the performance of the hybrid robot control system. For that purpose, two optimization techniques followed by fine-tuning are proposed and simulated to get the optimized PID parameters. The first proposed optimization method applies the Satin Bowerbird (SB) optimization technique to optimize the PID parameters. The Crow Search Optimization (CSO) technique is applied to the SB results to improve the algorithm's performance and the PID parameters. The second proposed method applies the Emperor Penguin Optimization (EPO) technique for the optimization of the PID parameters. The results of both methods are fine-tuned. Moreover, a Kalman filter is used to improve the outcomes after and before tuning the PID parameters. Simulation results show that the proposed first method is more effective for the optimization methods of the PID controller, and its results outperform the results given by previously published research.