论文标题
软演员批判性的深度强化学习，用于容忍飞行控制
Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight Control
论文作者
论文摘要
容忍失误的飞行控制面临挑战，因为为每个意外故障开发基于模型的控制器是不现实的，并且在线学习方法由于样本效率低而可以处理有限的系统复杂性。在这项研究中，提出了一个可以承受多种故障类型的喷气飞机的无模型耦合型飞行控制器。经过离线训练的级联软演员批判性深钢筋学习控制器在高度耦合的操作上取得了成功，其中包括协调的40度银行攀爬转弯，平均绝对误差为2.64％。该控制器对六个故障病例具有鲁棒性，包括rud剂量为-15 ver，副翼有效性降低了70％，结构性衰竭，结冰和向后的C.G.随着响应的稳定，攀岩转弯即可成功完成。还展示了对偏置传感器噪声，大气干扰以及改变初始飞行条件和参考信号形状的鲁棒性。
Fault-tolerant flight control faces challenges, as developing a model-based controller for each unexpected failure is unrealistic, and online learning methods can handle limited system complexity due to their low sample efficiency. In this research, a model-free coupled-dynamics flight controller for a jet aircraft able to withstand multiple failure types is proposed. An offline trained cascaded Soft Actor-Critic Deep Reinforcement Learning controller is successful on highly coupled maneuvers, including a coordinated 40 degree bank climbing turn with a normalized Mean Absolute Error of 2.64%. The controller is robust to six failure cases, including the rudder jammed at -15 deg, the aileron effectiveness reduced by 70%, a structural failure, icing and a backward c.g. shift as the response is stable and the climbing turn is completed successfully. Robustness to biased sensor noise, atmospheric disturbances, and to varying initial flight conditions and reference signal shapes is also demonstrated.
