Adaptive Trajectory Control for Quadcopter using Extended Kalman Filter-Based Self-Tuning PID under Gaussian Disturbances
DOI:
https://doi.org/10.18196/jrc.v6i5.27955Keywords:
Self-Tuning PID, Extended Kalman Filter, Quadcopter Control, Trajectory Tracking, Gaussian DisturbanceAbstract
Quadcopters are known for their maneuverability, but their stability is often challenged in changing environments. The PID parameters are adjusted manually so it is less adaptive. This research introduces the combination of Self-Tuning Proportional-Integral-Derivative (PID) and Extended Kalman Filter (EKF). A PID controller adjusts parameters based on errors and state estimates obtained from the EKF in real time. The disturbances used are Gaussian random disturbances on the system and sensors, simulated a normal distribution using the Simulink model. The basic PID parameters are determined through numerical simulations, then adaptively calibrated with a multiplier function based on estimation and error. The contributions of this study are: (1) developing an EKF-based SelfTuning PID control for a quadcopter system; (2) demonstrating an adaptive response to disturbances through simulations; and (3) presenting an efficient tuning strategy suitable for resourcelimited systems. From simulation, z-axis overshoot is successfully decreased from 7.37% to only 2.54%, while the steady-state error remains low under system disturbances. Computational efficiency is achieved because 12 state variables are controlled using a single set of global PID parameters, and the tuning process takes place in real time without relying on complex AI-based optimization methods. The proposed control approach is able to maintain trajectory tracking accuracy in three-dimensional space adaptively and with efficient resource usage. These results demonstrate that the EKF-PID method is effective for UAV control in dynamic and disruptive environments.
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