We propose a game-theory-based deep-learning tracking control scheme to enable a holonomic flying system to perform an autonomous trajectory tracking task, when considering saturating actuators, adversarial inputs, and nonquadratic cost functionals. The problem is formulated as a two-player zero-sum game, whose online solution is computed by learning the saddle point strategies in real time. Three approximators, namely a critic and two actors, are tuned online using data generated in real time along the system trajectories. The adaptive control character of the algorithm requires a persistence of excitation condition to be a priori validated, which is relaxed by using. a deep-learning approach, based on experience replay with multiple layers. A robustifying control term is added to eliminate the effect of residual errors, leading to asymptotic stability of the equilibrium point of the closed-loop system. A simulation of a target tracking application, where the measurements available to the aerial system are perturbed by persistent adversaries, is performed to validate the effectiveness of the proposed approach.

中文翻译：

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对抗性输入下自主飞行系统的深度学习跟踪

我们提出了一种基于博弈论的深度学习跟踪控制方案，使完整飞行系统能够在考虑饱和执行器、对抗性输入和非二次成本函数时执行自主轨迹跟踪任务。该问题被表述为一个两人零和博弈，其在线解决方案是通过实时学习鞍点策略来计算的。三个近似器，即一个评论家和两个演员，使用沿系统轨迹实时生成的数据在线调整。该算法的自适应控制特性需要先验验证激励条件的持久性，使用时可以放宽。一种深度学习方法，基于多层的经验回放。添加了一个健壮控制项以消除残差的影响，导致闭环系统平衡点的渐近稳定性。执行目标跟踪应用程序的模拟，其中空中系统可用的测量受到持久对手的干扰，以验证所提出方法的有效性。