Optimizing the Output System of PG36M555 DC Carbon-Brush Motors Using LQR and LQT Methods in MATLAB Simulink

Abstract

This research focuses on optimizing the output system of the PG36M555 DC carbon-brush motor using Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT) methods implemented in MATLAB Simulink. DC motors, particularly carbon-brush types, are widely used in robotics, industrial automation systems, and other engineering applications due to their compact size, high torque, and efficiency. However, maintaining output precision and stability under varying operational conditions remains a significant challenge, especially in dynamic environments with load fluctuations and external disturbances.

To address these issues, a combination of simulation and experimental validation was applied to ensure the effectiveness of the proposed control strategies. The LQR method focuses on minimizing overshoot and improving system stability by optimizing control gains, while the LQT method enhances tracking performance by accurately following predefined reference signals. Simulation results demonstrated that the LQR method reduced overshoot by 25% and improved stability compared to traditional PID controllers. Meanwhile, the LQT method improved tracking accuracy by 30%, making it highly suitable for applications requiring precise motion control.

Experimental validation was conducted using physical setups of the PG36M555 motor, confirming the simulation results with deviations of less than 5%. These findings emphasize the significant potential of LQR and LQT methods in optimizing DC motor performance, particularly in applications demanding precise control, stability, and energy efficiency. By integrating advanced simulation tools and experimental analysis, this study contributes to the development of robust control strategies for advanced engineering applications.