Optimized Leader-Follower Consensus Control of Multi-QUAV Attitude System Using Reinforcement Learning and Backstepping

This work is to explore the optimized leader-follower attitude consensus scheme for the multi-quadrotor unmanned aerial vehicle (QUAV) system. Since the QUAV attitude dynamic is modeled by a second-order nonlinear differential equation, the optimized backstepping (OB) technique can be competent for this control design. To derive the optimized leader-follower attitude consensus control, the critic-actor reinforcement learning (RL) is performed in the final backstepping step. Different with the attitude control of single QUAV, the case of multi-QUAV is composed of multiple intercommunicated QUAV attitude individuals, so its control design is more complex and thorny. Moreover, the traditional RL optimizing controls deduce the critic or actor updating law from the negative gradient of approximated Hamilton-Jacobi-Bellman (HJB) equation' square, thus it leads to these algorithms very complexity. Hence the traditional optimizing control methods are implemented to multi-QUAV attitude system difficultly. However, since this optimized scheme deduces the RL training laws from a simple positive function of equivalent with HJB equation, it can obviously simplify algorithm for the smooth application in the multi-QUAV attitude system. Finally, theory and simulation certify the feasibility of this optimized consensus control.