Imitation Reinforcement Learning Attitude Controller for Fixed-Wing UAVs

The low-level control problem of fixed-wing unmanned aerial vehicles(UAVs) is complex and challenging. Although reinforcement learning (RL) algorithms are widely applied in the field of robot control, their convergence speed is typically slow due to the sparsity of rewards. An imitation reinforcement learning(IRL) algorithm based on proximal policy optimization(PPO) algorithm for attitude control of fixed-wing UAVs has been proposed. The strategy of imitation learning enables the avoidance of ineffective and perilous exploration during the initial stages of training. The nonlinear adaptive distribution rejection control (NLADRC) algorithm has been devised as the imitated agent, and a smooth transition strategy has been proposed to effectuate the switch from imitation learning to reinforcement learning. A series of simulation experiments demonstrate that the proposed strategy significantly improves the quality of reinforcement learning training, and even in strong gale wind environments, it still demonstrates superior control effectiveness compared to the imitated agent.