Performance Comparison Between Pid And Lqr Controllers For Drone Application - Zamree, Alif Imran (Universiti Sains Malaysia, 2021)
In industries of unmanned aerial vehicle (UAV), the implementation of a motor control system is essential to ensure the system mechanism can be operated efficiently. In addition, DC servo motor systems are widely applied in a variety of fields of UAV. They are used to generate electrical power in power plants and to supply mechanical motive power to operate the UAV and manage numerous industrial operations in industrial settings. In some application of the DC servo motor, when load is applied, or disturbance occur during the operation, the DC servo motor is required to maintain its desired speed to ensure the stability and efficiency of the system. This system can be controlled using PID, Fuzzy, LQR and other more. The PID algorithm becomes a closed loop system when it is added to the motor. The system is developed in MATLAB software, and the PID algorithm is tuned by adjusting the values of proportional gain, Kp, integral gain, Ki, and derivative gain, Kd to get a motor speed and position that is less overshoot, has a longer settling time, and has a longer rise time. To control the Dc servo motor speed and position, the Linear Quadratic Regulator (LQR) controller is introduced. The LQR controller is designed and tuned using MATLAB/Simulink, and it is simulated using a mathematical model of a DC servo motor. A new approach of controlling the motor is the Linear Quadratic Regulator (LQR) controller. The Linear Quadratic Regulator (LQR) is an optimum control theory that focuses on controlling a dynamic system at the lowest possible cost. The purpose of the Linear Quadratic Regulator (LQR) is to minimize the deviation of the motor's speed and position. The input voltage of the motor will be specified by the motor's speed, and the output will be compared to the input. The advantages of using LQR are that it is simple to build and that it improves the accuracy of state variables by estimating them. When contrasted to pole placement, the LQR control has the advantage of specifying a set of performance weighting rather than needing to define where eigenvalues should be positioned, which may be more intuitive.