Formation Control of Multiple Unmanned Aerial Vehicles by Event-Triggered Distributed Model Predictive Control

Abstract

This paper proposes an event-triggered model predictive control (MPC) scheme for the formation control of multiple unmanned aerial vehicles (UAVs). A distributed MPC framework is designed in which each UAV only shares the information with its neighbors, and the obtained local finite-horizon optimal control problem (FHOCP) can be solved by a swarm intelligent optimization algorithm. An event-triggered mechanism is proposed to reduce the computational burden for the distributed MPC scheme, which takes into consideration the predictive state errors as well as the convergence of cost function. Furthermore, a safe-distance-based strategy for no-fly zone avoidance is developed and integrated into the local cost function for each FHOCP. Numerical simulations show that the proposed event-triggered distributed MPC is more computationally efficient to achieve formation control of multiple UAVs in comparison with the traditional distributed MPC method.