PID controller with Kalman filter design for quadcopter DJI F450 system - Mohd Mokhtar, Nur Salma (2023)
This thesis presents the design of quadcopter DJI F450 stability controller design that consists of PID and Kalman filter. Quadcopter DJI F450 is a type of UAV that for unmanned aerial vehicle. Deeper in the type of the aircraft is it’s a VTOL where the long term is vertical takeoff and landing vehicle where it has the capability of taking off from the ground without having to have accelerating. Flight stability for an aerial vehicle is important because of the disturbance of external forces in higher altitude can affect it even more. When having noise inside the feedback signal, a PID only closed loop controller may have difficulties in maintaining the stability of the quadcopter, and a prediction filter may can solve this problem, thus Kalman filter is one of the prediction control systems that can be used. Usually for the reading of the attitude sensor that is supposed to be the feedback of the overall control system have some in accuracy problem, where the reading might have some continuously spike error where the signal input might not be stable for the input feed of the system. Kalman Filter is one of a great method to smoothen out the sensor signal by making predictive signal output. From here the motivation of designing PID and PID KF was initiated to implement on the quadcopter DJI F450 using simulation which is MATLAB and Simulink. In this research the mathematical model of the quadcopter DJIF450 were used, and the attitude sensor input were used from the actual quadcopter attitude with a small delay, and a random noise were added to the signal that would imitate the real sensor. Three environments were set up and compare which are the first experiment not using a PID and KF, second experiment only using PID and the last using both PID and KF and the result from the three experiments were evaluate. The results show that indeed using the combination of PID and KF shows the most stable flight and higher accuracy based on the desired attitude given because of the sensor output filtered from the KF as the input of the PID.