Multiquadrotor Formation Tracking With Mixed Constraints: A Hierarchical Rolling Optimization Approach

This article investigates the formation tracking problem of multiquadrotor unmanned aerial vehicles (UAVs) that are subject to mixed constraints including practical constraints on the velocities, attitudes, and input forces. These constraints present significant challenges in regulating and controlling the UAVs. We propose a hierarchical control strategy with a local layer based on model predictive control (MPC) and a cyber layer based on cooperative rolling optimization control (CROC) to simultaneously solve the tracking problems and ensure that corresponding physical states conform to the mixed constraints. In the cyber layer, each node only needs to receive the information from its neighbor nodes to perform synchronous rolling optimization. The cost function of the local open-loop rolling optimization problem is constructed by penalizing the estimated predicted (E-predicted) trajectory and the estimated assumed (E-assumed) trajectory error in the cyber layer. In the local layer, we propose a control algorithm to solve the mixed constraint problem of the cascade control of the quadrotors, which consists of inner-loop attitude control and outer-loop trajectory tracking control. Finally, the simulation results are presented to analyze the performance and the effectiveness of the proposed algorithm.