In this paper, a model‐free optimal synchronization controller is designed to achieve the aggressive attitude synchronization for multiple heterogeneous quadrotor systems with highly nonlinear and coupled dynamics by using a reinforcement learning (RL) approach. A distributed observer is first designed for each following quadrotor to estimate the states of a virtual leader. A performance function is then utilized for each quadrotor to penalize the observed synchronization error and the control effort. An RL approach is finally employed to learn the optimal control law without any knowledge of the dynamic model information of the followers. The control law depends on the quadrotor states and the observer states, and guarantees that the attitude synchronization error converges to zero for all quadrotors, under aggressive maneuvers. Simulation results are provided to verify the effectiveness of the proposed controller.

中文翻译：

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通过强化学习实现多个异构四旋翼的无模型姿态同步

在本文中，通过使用强化学习（RL）方法，设计了一种无模型的最佳同步控制器，以实现具有高度非线性和耦合动力学的多个异构四旋翼系统的主动姿态同步。首先为每个随后的四旋翼飞行器设计一个分布式观察器，以估计虚拟前导器的状态。然后将性能函数用于每个四旋翼，以惩罚观测到的同步误差和控制工作量。最终，采用RL方法来学习最优控制律，而无需了解跟随者的动态模型信息。控制定律取决于四旋翼状态和观察者状态，并保证在激进操纵下所有四旋翼的姿态同步误差收敛为零。