Artificial Intelligence to Aid the Efficient Dynamic Behavior of the Dual-axis Solar Tracking System

Renewable energy applications rely heavily on the use of Artificial Intelligence and machine learning. The new global energy economy is gaining momentum as photovoltaic solar energy accelerates worldwide. Several solar panels have been installed in the direction of maximum solar radiation for various applications around the world. But in the case of moving platforms, for instance, an application like ships, military vehicles, satellites, etc., the maximum solar radiation at all the positions and displacements is not obtained. In addition, the sun is still in motion depending on the variation of the calendar. Consequently, there are problems with the energy collected by solar panels and their production which differs considerably at different times, positions, and bearings. This research work aims to model the dynamic behavior of a 2-DOF mechanism that can be used as a dual-axis solar moving base. As a verification, the equation of motion examines several important issues in implementing an expert system for the robust controller design of the proposed intelligent mechanism. Accordingly, the proposed algorithm of moving bases and tracking systems is the most efficient way to get maximum radiation and therefore implement maximum productivity with minimum losses for faster displacement of moving platforms with solar panels. It is evident that the movement of the panels toward the direction of solar motion uses the maximum radiation at all times, and as a result, the higher efficiency of the solar panels is achieved.