Analytic and robust solar tracking solution using the linear quadratic tracking and the entropy on fuzzy logic control

This study discusses solar tracker on solar panel and single-axis photovoltaic (PV) tracking systems using a tilt panel and an electric motor. This research focuses on passive motion in solar panel. Passive motion uses manual settings based on the calculation of the solar position. This research used Linear Quadratic Tracking (LQT) and Fuzzy Entropy (FE) methods. LQT was chosen because the analytical method produces an optimal solution. While the comparison, the fuzzy entropy method is a performance modification of the Fuzzy Logic Control (FLC) on the solar tracking motor. The results obtained using the LQT and FE methods responded quite well with a small risetime and settling time and did not experience overshoot. Statistically, performance is measured by mean absolute error (MAE). The measured MAE depends on the simulation time, that is, the longer the time, the smaller the measured MAE. It is because FE is able to learn the precision condition and conclude it as a controller in the next iteration. The comparison obtained is that FE is able to learn the solar tracking process very quickly, so that the performance obtained is close to the results obtained from the LQT analytical solution.