Adaptive Optimization Tracking Control for an Unmanned Aerial Vehicle with Disturbance Suppression

Aiming at the tracking control for an unmanned aerial vehicle (UAV) with uncertainty and external disturbances, an adaptive optimization control method is proposed based on the backstepping method. The model uncertainty in the UAV is estimated using neural networks. Moreover, a disturbance observer is designed to estimate and counteract the disturbance in real-time. In addition, reinforcement learning (RL) is used to solve optimal problems and overcome the difficult issues of the Hamilton-Jacobi-Bellman (HJB) equation. In the controller design, dynamic surfaces are employed to avoid complex derivation issues in the virtual controller and improve operating efficiency. Furthermore, the stability of the UAV system is proved by the Lyapunov theory. Finally, the effectiveness and superiority of the proposed method are verified through numerical simulations and real-world experiments.