针对旋翼无人机飞行控制系统，提出了一种新型模仿学习驱动的飞行控制方法，通过设计非线性干扰观测器预先采集先验飞行数据，采用深度强化学习网络架构，设计模仿-探索结合的学习训练策略，有效提高了旋翼无人机对外界干扰和自身不确定因素的鲁棒性。首先，通过分析旋翼无人机的动力学模型，采用姿态与位置分层的控制框架，提出分层控制架构下的鲁棒观测控制方法，利用实时估计系统中的不确定性，并充分采样旋翼无人机的飞行数据，构建模仿学习数据池。接下来，利用采样的数据池训练学习控制策略。具体而言，首先设计深度确定性策略梯度网络架构(Deep Deterministic Policy Gradient, DDPG)实时输出不确定性估计，通过两步的网络参数训练，实现旋翼无人机鲁棒飞行控制。首先，通过设计兼顾单步和多步的混合损失函数，使用非线性干扰观测器采样的轨迹离线训练DDPG网络。在此基础上，进一步设探索驱动的强化学习训练，通过混合采样模仿学习经验池和探索经验池，利用优先经验回放策略更新DDPG网络参数。通过设计非线性干扰观测器采样数据，提出探索与经验结合的模仿-探索学习机制, 充分利用模型驱动数据和学习探索数据，有效提高了旋翼无人机的飞行控制控制精度与鲁棒性。仿真与实物实验结果表明，该方法在不同飞行任务及扰动条件下均能够实现稳定飞行与有效轨迹跟踪。具体而言，在螺旋轨迹跟踪任务中，三轴飞行误差均远小于0.1 m；在高速飞行任务中，无人机在6 s内的最大飞行速度超过2 m/s；在转弯飞行任务中，实际飞行速度与期望飞行速度的整体变化趋势基本一致。实物飞行实验进一步表明，实际飞行轨迹与期望轨迹总体一致，验证了该方法在真实飞行条件下的有效性与鲁棒性。

In this paper, an imitation-learning-driven robust flight control method is proposed for multirotor UAVs. Prior flight data collected by a nonlinear disturbance observer are combined with deep reinforcement learning exploration to improve training efficiency and enhance robustness against external disturbances and model uncertainties. Based on the multirotor dynamic model and a hierarchical attitude--position control framework, the flight control task is formulated as a robust trajectory-tracking problem, and an observer-based control scheme is developed to estimate uncertainties online and generate demonstration data for imitation learning. A Deep Deterministic Policy Gradient (DDPG) network is then introduced to learn the robust flight control policy. The training process consists of two stages. In the first stage, the network is offline initialized using observer-generated trajectories and a hybrid loss function that accounts for both single-step and multi-step learning errors. In the second stage, the network is further optimized through exploration-driven reinforcement learning with prioritized experience replay over mixed samples from the demonstration buffer and the exploration buffer. By combining model-based prior data with online exploration data, the proposed imitation--exploration learning scheme improves both tracking accuracy and robustness. Simulation and real-flight experiments show that the proposed method achieves stable flight and effective trajectory tracking under different flight tasks and disturbance conditions. In the spiral trajectory-tracking task, the tracking errors along all three axes remain below 0.1~m. In the high-speed flight task, the maximum flight speed exceeds 2~m/s within 6~s. In the turning flight task, the measured flight speed follows the reference trend well. Real-flight results further show good agreement between the actual and desired trajectories, confirming the effectiveness and robustness of the proposed method.