This paper introduces a new formulation of dynamic control allocation as a dynamic optimization problem. This optimal control formulation allows us to develop allocation with guaranteed actuator constraints, and to learn the optimal control allocation online using measured data and without knowing the system dynamics. The general solution to this problem is provided in the form of an $H^{\infty }$controller. Current results for static control allocation are shown to be a special case of the general dynamic optimization solution. Reinforcement learning is used to find the optimal solution for the constrained actuators problem. The methods proposed in the paper are tested on a F-16 flight simulation.

本文介绍了将动态控制分配作为动态优化问题的新公式。这种最优控制公式使我们能够在保证执行器约束的情况下进行分配，并在不了解系统动态的情况下使用测量数据在线学习最优控制分配。此问题的一般解决方案以$H^{\infty }$控制器。静态控制分配的当前结果显示为一般动态优化解决方案的特例。强化学习用于找到约束执行器问题的最佳解决方案。本文提出的方法在F-16飞行模拟中进行了测试。