Optimal Containment Control of Multiple Quadrotors via Reinforcement Learning*

Abstract

This paper explores the optimal containment control problem for nonlinear and underactuated quadrotors with multiple team leaders governed by nonlinear dynamics, employing the reinforcement learning. A cascade controller is formulated, comprising a position control component to ensure containment achievement and an attitude control component to govern rotational channel. The proposed optimal control protocols derived from historical data collected from quadrotor systems without requirement for exact knowledge of vehicle dynamics. The simulation illustrates the effectiveness of the proposed controller in managing a quadrotor team with multiple leaders.