Optimal Containment Control of a Quadrotor Team With Active Leaders via Reinforcement Learning

This article proposes an optimal controller for a team of underactuated quadrotors with multiple active leaders in containment control tasks. The quadrotor dynamics are underactuated, nonlinear, uncertain, and subject to external disturbances. The active team leaders have control inputs to enhance the maneuverability of the containment system. The proposed controller consists of a position control law to guarantee the achievement of position containment and an attitude control law to regulate the rotational motion, which are learned via off-policy reinforcement learning using historical data from quadrotor trajectories. The closed-loop system stability can be guaranteed by theoretical analysis. Simulation results of cooperative transportation missions with multiple active leaders demonstrate the effectiveness of the proposed controller.