Nonlinear Model Predictive Formation Flight

Abstract

This correspondence paper presents the validation of a formation flight control technique with obstacle avoidance capability based on nonlinear model predictive algorithms. Control architectures for multi-agent systems employed in this correspondence paper can be categorized as centralized, sequential decentralized, and fully decentralized methods. Centralized methods generally have better performance than decentralized methods. However, it is well known that the performance of the centralized methods for formation flight degrades when there exists communication failure among the vehicles, and they require more computation time than the decentralized method. This correspondence paper evaluates the control performance and the computation time reduction of the sequential decentralized and fully decentralized methods in comparison with the centralized method and shows that the fully decentralized method can be made effective against short term communication failure. The control inputs for formation flight are computed by nonlinear model predictive control (NMPC). The control input saturation and state constraints are incorporated as inequality constraints using Karush Kuhn Tucker conditions in the NMPC framework, and the collision avoidance can be considered in real time. The proposed schemes are validated by numerical simulations, which include the process and measurement noise for more realistic situations.