Modeling of LQR and LQT Control on DC Motor C34-L-60

Abstract

Control systems play a crucial role in the industrial world, ensuring optimal performance of a plant or process. A well-designed control system is characterized by its ability to produce a responsive and accurate output that closely aligns with the desired setpoint. This responsiveness is essential for maintaining efficiency and precision in industrial operations. In this research, the control methodology begins with the modeling of the system using MATLAB software. The study focuses on two advanced control techniques: Linear Quadratic Regulator (LQR) and Linear Quadratic Tracker (LQT). These techniques are applied to a DC Motor model, specifically the C34-L-60 motor, which serves as the primary plant in this investigation. The system modeling process involves creating a detailed representation of the motor dynamics and integrating the control algorithms to simulate its performance. By leveraging MATLAB's powerful simulation tools, the study aims to derive optimal control parameters for the motor under various operating conditions. This step is critical to achieving a comprehensive understanding of how each control method influences the system's behavior. Once the models are established, simulations are conducted to evaluate the performance of both the LQR and LQT control strategies. The simulations allow for a detailed comparison of their respective responses to the desired setpoints. Key performance metrics such as settling time, overshoot, and steady-state error are analyzed to determine the effectiveness of each control method. The data obtained from these simulations highlights the differences between the LQR and LQT methods. LQR focuses on minimizing the quadratic cost function for state variables and control inputs, while LQT extends this approach by considering tracking performance for specific reference trajectories. These distinctions provide valuable insights into the applicability of each technique for different industrial scenarios. Overall, this research emphasizes the importance of selecting an appropriate control method to achieve optimal system performance. By comparing the simulation results of LQR and LQT, the study contributes to a deeper understanding of their capabilities and limitations in controlling DC motors. This knowledge serves as a foundation for further advancements in industrial control system design.