Radial Basis Function Network Based Self-Adaptive PID Controller for Quadcopter: Through Diverse Conditions

(1) Nur Hayati Sahrir Mail (Universiti Teknologi Malaysia, Malaysia)
(2) * Mohd Ariffanan Mohd Basri Mail (Universiti Teknologi Malaysia, Malaysia)
*corresponding author


A quadcopter is an underactuated and nonlinear system which requires a robust controller to aid in maneuvering the quadcopter during flight. A Proportional-Integral-Derivative (PID) controller is easy and suitable to implement, and its efficiency is proved in quadcopter control. However, a PID controller with fixed parameters is inadequate enough to control a quadcopter system with different inputs or perturbations. This paper proposes the development of a self-adaptive PID controller assisted by Radial Basis Function (RBF) Network, to improve the function of the PID controller and help a quadcopter to better adapt towards different inputs and situations, independently.  This work contributes to introducing RBF-PID controller to adaptively fly the underactuated quadcopter through different trajectory and perturbations using simulation. By using the hidden Gaussian function to train the current input, estimate the suitable output and update the Jacobian Information during system control, the PID gains can change adaptively during flight, additionally with the help of Gradient Descent Method (GDM). The proposed method is compared to the traditional PID controller tuned using the PID Tuner App in Simulink. Different inputs are given to test the altitude, attitudes, and position tracking such as step, multistep, sine wave, circular and lemniscate trajectory. The simulated results proved the robustness of RBF-PID in enhancing the disturbance rejection capacity by 13% to 25% in the presence of perturbations (sine wave and wind gust) compared to PID controller. The proposed controller can ensure quadcopter’s flight stability through perturbations that is within the quadcopter’s limitations.


Neural Network; PID Controller; Quadcopter UAV; Radial Basis Function




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