DC Motor A-max 108828 and Noise using LQR and LQT Methods - Rachma Prilian Eviningsih; Anggara Trisna Nugraha; Rama Arya Sobhita (Journal of Marine Electrical and Electronic Technology, 2025)
Technology is continuously evolving in this era to meet various needs and facilitate human work, making tasks more efficient and automated. One of the significant developments in the field of electrical engineering is the Direct Current (DC) motor, which has become an essential component in many industrial applications. Due to its flexibility, controllability, and efficiency, DC motors remain a crucial subject of research and development in the industrial sector. Their ability to provide precise speed and torque control makes them ideal for applications ranging from robotics and automation to transportation and manufacturing. This paper focuses on the implementation of DC motors using two advanced control methods: Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT). The LQR method is widely used in control engineering due to its ability to optimize system stability and minimize control effort while maintaining desired performance. Meanwhile, the LQT method is an extension of LQR, designed to further enhance tracking accuracy by reducing errors that occur during the tracking process. By applying the LQT approach, the system is expected to achieve a more accurate response, minimizing deviations from the reference trajectory even in the presence of external disturbances and noise. In this study, a DC motor model A-max 108828 is used as the plant, representing the real-world system to be controlled. The motor system is first mathematically modeled to establish its dynamic behavior and then transitioned into a simulation environment using Matlab Simulink software. The simulation evaluates the performance of the LQR and LQT controllers, comparing their ability to maintain system stability, reduce steady state errors, and respond to external disturbances effectively. Additionally, the effect of noise on system performance is analyzed to assess the robustness of each control strategy. The results obtained from the simulations demonstrate that both LQR and LQT methods significantly improve the performance of the DC motor system, with LQT exhibiting better tracking precision due to its enhanced error correction mechanism. The study also highlights the advantages of optimal control techniques in achieving efficient and stable motor operations, making them highly applicable for real-world industrial automation.