  In many specific scenarios, accurate and effective system identification is a
commonly encountered challenge in the model predictive control (MPC)
formulation. As a consequence, the overall system performance could be
significantly weakened in outcome when the traditional MPC algorithm is adopted
under those circumstances when such accuracy is lacking. This paper
investigates a non-parametric closed-loop behavior learning method for
multi-agent motion planning, which underpins a data-driven predictive control
framework. Utilizing an innovative methodology with closed-loop input/output
measurements of the unknown system, the behavior of the system is learned based
on the collected dataset, and thus the constructed non-parametric predictive
model can be used to determine the optimal control actions. This non-parametric
predictive control framework alleviates the heavy computational burden commonly
encountered in the optimization procedures typically in alternate methodologies
requiring open-loop input/output measurement data collection and parametric
system identification. The proposed data-driven approach is also shown to
preserve good robustness properties. Finally, a multi-UAV system is used to
demonstrate the highly effective outcome of this promising development.
