Zamree, Alif Imran
(2021)
Performance Comparison Between Pid And Lqr Controllers For Drone Application.
Project Report.
Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Aeroangkasa.
(Submitted)
Abstract
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.
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