Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

Neural network (NN) methods have become increasingly efficient and applicable in control. This article presents a novel nested control strategy based on adaptive radial basis function neural networks (RBFNN) and NN supervised control embedded with integrator back stepping (IBS) for a robust position and attitude trajectory tracking of quadrotor aerial robot in presence of modeling uncertainties, sensing noise, and external bounded disturbance. The control philosophy is derived from the decentralized inverse dynamics, where the adaptive RBFNN is adopted for the outer loop to approximate the unknown external disturbance, and adaptively compensate for their effect. In conjunction with a supervised control that stabilizes the attitude in the inner loop by adaptively compensating the unknown and unmodeled uncertainties, avoiding initial instability of the attitude during NN convergence. Furthermore, noise is attenuated by an adaptive extended Kalman filter. Stability analysis was elaborated using Lyapunov stability theory. Simulation of adaptive RBFNN control philosophy proved robustness and effectiveness compared to proportional integral derivative, IBS, and offline decentralized convolution neural network algorithms as it generates the control law by guarantying a fast convergence of parameters, better external disturbance compensation, and noise attenuation. The proposed scheme of robust tracking was validated.