In the modern era, the development of renewable energy technology has rapidly advanced, and one key approach to improving technology is through system optimization. System optimization involves solving problems to achieve the best possible conditions, resulting in maximum or minimum values for system performance. In the context of DC motors, optimization aims to reduce the initial peak in motor current when the motor is started, ensuring smoother and more efficient operation. Various optimization techniques can be employed, with two commonly used circuits being the Linear Quadratic Regulator (LQR) and the Linear Quadratic Tracking (LQT). Both methods are designed to improve system performance by controlling motor behavior, but they differ in their approach to dynamic feedback and tracking error. This paper explores these circuits, using a BN12 type DC motor as a case study, to illustrate how LQR and LQT can be applied to minimize startup current peaks and enhance motor control. Through simulation and analysis, the study demonstrates how each circuit contributes to achieving optimal performance in DC motor systems. The results highlight the potential of these optimization methods in improving the efficiency and longevity of DC motors, particularly in renewable energy applications where motor performance is crucial. This research aims to provide insights into the practical applications of LQR and LQT for optimizing DC motor systems in various technological fields.