Non-Parametric Behavior Learning for Multi-Agent Decision Making With Chance Constraints: A Data-Driven Predictive Control Framework

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 degraded in outcome when the traditional MPC algorithm is adopted under those circumstances when such accuracy is lacking. To cater to this rather major shortcoming, this paper investigates a non-parametric behavior learning method for multi-agent decision making, which underpins an alternate 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 for the determination of optimal control actions. This non-parametric predictive control framework attains the noteworthy key advantage of alleviating the heavy computational burden commonly encountered in the optimization procedures otherwise involved. Such requisite optimization procedures are typical in existing methodologies requiring open-loop input/output measurement data collection and parametric system identification. Then with a conservative approximation of probabilistic chance constraints for the MPC problem, a resulting deterministic optimization problem is formulated and solved effectively. This intuitive data-driven approach is also shown to preserve good robustness properties (even in the inevitable existence of parametric uncertainties that naturally arise in the typical system identification process). Finally, a multi-drone system is used to demonstrate the practical appeal and highly effective outcome of this promising development.