Trajectory Tracking Control for Fixed-Wing UAV Based on DDPG

The study proposes a method for the trajectory tracking control of a fixed-wing unmanned aerial vehicle (UAV) based on the deep deterministic policy gradient (DDPG). First, the problem of controlling the trajectory of a fixed-wing UAV is combined with the reinforcement learning framework and transformed into a Markov decision process, and a DDPG agent is established in the framework of TensorFlow. Second, we conducted simulations to train and optimize the model in a 3D environment of trajectory tracking control and obtained an integrated DDPG-based trajectory tracking controller that can regulate functions ranging from the state of flight of the UAV to rudder control. Third, we constructed a digital simulation system to verify the proposed method while considering the influence of parametric uncertainties, measurement-induced noise, and delays in the response of the control system. The effectiveness and robustness of the proposed DDPG controller were verified by comparing its performance with that of traditional proportional-integral-derivative (PID) control. Unmanned aerial vehicles have gained widespread use across numerous sectors of society. However, the control process for fixed-wing UAVs is more intricate and challenging than that for rotor type UAVs due to the coupled and nonlinear nature of their dynamic models. To address this issue, decoupling and splitting hierarchical control methods are commonly employed. However, these methods can introduce errors and affect flight performance. In contrast, artificial intelligence, particularly deep learning and reinforcement learning, has made significant advancements and found successful applications in areas such as robot control and gaming. To address the challenges posed by complex forms, excessive measurement information, and manual parameter tuning associated with traditional control approaches for fixed-wing UAVs, this paper introduces a deep reinforcement learning algorithm, i.e., the deep deterministic policy gradient, into the control process of fixed-wing UAVs. The paper presents a trajectory tracking control policy for UAVs, which efficiently achieves the task of tracking a desired trajectory without the need for selecting multiple working state nodes or designing linear control laws. Further, through simulation verification, the proposed policy demonstrates adaptability to interference caused by parameter bias, measurement noise, and control system response delays while achieving control accuracy comparable with classical control methods.