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

Micro particles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. In the present paper, a novel hybrid algorithm based on Arithmetic optimization algorithm (AOA) and Artificial Gorilla troop’s optimization (GTO), (HAOAGTO) is compared with different four algorithms Arithmetic optimization algorithm (AOA), Artificial Gorilla troop’s optimization (GTO), Seagull optimization algorithm (SOA), Parasitism-predation Algorithm (PPA). 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. We verified that the obtained results by application of the proposed hybrid algorithm-based AOA and GTO (HAOAGTO) is better than those obtained by Arithmetic optimization algorithm (AOA), Artificial Gorilla troop’s optimization (GTO), Seagull optimization algorithm (SOA), Parasitism-predation Algorithm (PPA). it is implemented to obtain the optimal parameters of the PID for reduction the ISTES.