Full Backstepping Control in Dynamic Systems With Air Disturbances Optimal Estimation of a Quadrotor

Tracking trajectory with high precision under wind perturbations is a difficult problem of a quadrotor. In response to this problem, a novel wind perturbation estimator using neural network is proposed. The structure of wind estimator is designed based on deep understanding of a quadrotor, and training process is optimized to solve overfitting problem in the presence of sensor noise. With the consideration of wind perturbations and rotor dynamics, cascaded Lyapunov functions are used to derive full backstepping controller. Compared with traditional wind estimator, the proposed estimator is simple to be carried out and shows better robustness to sensor noise. To the best of our knowledge, the proposed controller is more robust to wind with less power cost than existing controllers. A series of simulations show the process to optimize wind estimator, and comparison between different controllers demonstrates that the proposed controller is robust to wind and energy-efficient. Finally, experiments have strengthened the effectiveness of our proposed method.