A hybrid meta-heuristic algorithm for optimum micro-robotic position control with PID controller

Abstract

The present paper aims to propose a novel hybrid algorithm, where the Arithmetic Optimization Algorithm (AOA) and Rat Swarm Optimization (RSO) are employed for the proportional-integral-derivative (PID) controller to control the position of a micro-robotics system. In the algorithm proposed, we combine the exploratory mechanisms of AOA with RSO's exploitative behaviors. The proposed algorithm is employed for identifying the PID controller optimal parameters considering six different objective functions. Using CEC 2017 benchmark functions, the proposed hybrid is evaluated, and these functions' performance is compared with the existing multiple algorithms. The statistical results are compared with the AOA, Jellyfish Search Optimization, and Harries Hawk Optimization algorithm for identifying the optimal PID controller settings considering multiple fitness functions. We consider performance indicators like PID controller parameters, rise time, settling time, and fitness values. The fetched simulation results revealed that, among all investigated fitness functions, the developed controller based on HAOARSO is the most effective algorithm for delivering global optimal solutions with less settling time and rise time, enabling the implementation on such optimization issues. Finally, the validation via MATLAB/Simulink simulations underscores the efficacy of the proposed algorithm.