Nonlinear Model Predictive Coordinated Standoff Tracking of a Moving Ground Vehicle

This paper proposes a nonlinear model-predictive control framework for coordinated standoff tracking by a pair of unmanned aerial vehicles. The benefit of this approach is to get optimal performance compared with using a decoupled controller structure: heading control for standoff-distance keeping and speed control for phase keeping. The overall controller structure is fully decentralized as each unmanned aerial vehicle optimizes its controller based solely on the future propagation of the pair vehicle states and the target estimates received via communication. This paper uses an acceleration model for sophisticated and realistic target dynamics, which can consider a more reasonable system noise covariance matrix reflecting the target's motion characteristics. To simplify optimization formulation and decrease computation burden, a new manipulation using the inner product of position vectors of the unmanned aerial vehicles with respect to the target position is proposed for antipodal tracking instead of using the relative phase-angle difference. To consider a more realistic situation, inequality constraints are considered for collision avoidance between unmanned aerial vehicles and control input saturations using penalty functions in the model-predictive control scheme. Simulations with a pair of unmanned aerial vehicles are done using realistic car trajectory data in an urban environment in the United Kingdom to verify the feasibility and benefit of the proposed approach with comparing to a Lyapunov vector field guidanceope