<p>An efficient solar tracking system is essential for maximizing energy harvesting. Traditional controllers often struggle with nonlinearities, external disturbances, and uncertainties. This paper presents a comparative performance analysis of three control strategies: proportional-integral-derivative (PID), model reference adaptive control (MRAC), and nonlinear model predictive control (NLMPC) for a dual-axis solar tracking system. Simulations are carried out over a 10,000 s horizon, under standard irradiance of 1000 W/m², ambient temperature of 25 °C, and actuator limits of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\pm 20\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>±</mo> <mn>20</mn> </mrow> </math></EquationSource> </InlineEquation> V. Diverse scenarios include standard tracking mode, external disturbance mode with a 7 N.m wind gust, and startup tracking mode. Results demonstrate that while the PID controller offers simplicity, it suffers from actuator saturation and persistent steady-state errors under disturbances. The MRAC provides adaptive capabilities but exhibits high overshoot and excessive energy consumption due to the adaptation mechanism. In contrast, the NLMPC consistently outperformed both, achieving the highest tracking efficiency of 99.98%, the lowest root mean square error of 0.0113, and a significant 83% reduction in motor energy consumption during large state transitions. These findings confirm that NLMPC provides the optimal balance between precision, robustness, and energy efficiency, making it the most viable solution for high-performance solar tracking systems.</p>