Intelligent Optimal Learning Control for Cooperative Formation Tracking of VTOL UAVs

A distributed intelligent optimal learning strategy is investigated for the formation tracking issue of a cluster of vertical-takeoff-and-landing unmanned aerial vehicles. Specifically, for the nominal error position system, a critic reinforcement learning (RL) force command is first developed, where a data-driven based update law is introduced. Then, a dynamics identifier is exploited to counteract the dynamics uncertainty of each UAV. A torque command by following the same development is also applied in the attitude loop tracking. Stability analysis indicates the uniform ultimate boundedness of the closed-loop systems. Simulation example validates the proposed strategy.