Robust Intelligent Self-tuning PID Controller for the Body-rate Stabilization of Quadrotors

This paper deals with the body-rate stabilization of quadrotors. The main idea is the design of a robust intelligent self-tuning PID controller for the quadrotor system. The key to successfully control a quadrotor with a PID controller is modeling the quadrotor dynamics accurately, then calculate the PID parameters based on the quadrotor identified model. However, the quadrotor is an uncertain underactuated system. Finding an accurate quadrotor model is a difficult task. Therefore, unlike the classical PID controller that depends on the system model, we propose an intelligent PID controller that employs an adaptive Wavelet Neural Network (WNN) to online estimate the optimal proportional, integral and derivative parameters. The adaptation laws of the PID parameters are derived using Lyapunov's stability method to guarantee the stability of the closed-loop system. The proposed intelligent controller does not require prior knowledge of the quadrotor dynamics. The proposed controller has a simple architecture that generates the body-rate commands to stabilize the quadrotor. The simulation results of the proposed intelligent self-tuning PID controller compared to the classical PID controller in stabilization of the quadrotor system prove the efficiency of the proposed scheme.