Tuning PID Control using GEO Algorithm for Dual Axis Sun Tracker System - Fiddina, Qori' Afiata; Pramujati, Windya Harieska; Buana, Arya Sula Cakra (Jurnal Kridatama Sains dan Teknologi, 2025)
Solar energy utilizes sunlight to produce electricity via photovoltaic (PV) panels, which act as a medium for capturing solar energy. PV panels are widely used for efficiency, cost-effectiveness, and scalability. Common challenges in solar energy capture include geographical location, weather conditions, and fluctuations in the direction of incoming sunlight. So, a control system is important. In countries with adequate sunlight, photovoltaic (PV) panels are designed with control systems that track the movement of the sunlight. Given the continuous motion and changing position of the sun, the development of advanced dual-axis solar trackers with intelligent control systems remains an ongoing area of research. Control methods used by sun tracker system is Proportional-Integral-Derivative (PID) with auto-tuning. For increase performance PID control is important to optimize PID at sun tracker sytem dual axis, simulated by MATLAB Simulink. GEO algorithm is optimization method using metaheuristic. GEO is founded on the intelligent adjustments on attack propensity and cruise propensity that golden eagles perform while searching for prey and hunting. In this study, the GEO algorithm will be used to optimize PID with a dual-axis solar tracker simulated by MATLAB Simulink. This research compares three model designs: uncontrolled, PID control using auto-tuning, and PID using GEO tuning (PID-GEO). The result of the simulation, we get comparison performance from three model designs is PID-GEO has the fastest settling time, smallest overshoot and undershoot of all model designs. Where the overshoot horizontal axis is 11. 372% and the undershoot is 0%, that also at the vertical axis, the overshoot is 11. 559% and undershoot is 0%. It can be concluded that PID-GEO has the best performance compared to PID auto-tuning and uncontrolled. So, this research concludes that GEO can be used to optimize PID control