Tuning PID Controllers Based on Hybrid Arithmetic Optimization Algorithm and Artificial Gorilla Troop Optimization for Micro-Robotics Systems

Abstract

Micro particles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. This paper attempts to provide a thorough comparison between five meta-heuristic search algorithms: Arithmetic optimization algorithm (AOA), Artificial Gorilla troop’s optimization (GTO), Seagull optimization algorithm (SOA), Parasitism-predation Algorithm (PPA), and hybrid between AOA and GTO (HAOAGTO). These approaches were used to calculate the PID controller optimal indicators with the application of different functions, including Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error ( (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). Every method of controlling was presented in a MATLAB Simulink numerical model. It is observed that the PPA technique achieves the highest values of best fitness value for simulation results among other control approaches, while the HAOAGTO approach reduces the best fitness function compared to other optimization techniques used. The results indicate that HAOAGTO is the best method among all approaches and that ISTES is the best choice of PID for optimizing the controlling parameters.