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.

翻译：在诸多特定场景中，准确有效的系统辨识是模型预测控制（MPC）框架中常遇到的挑战。因此，当采用传统MPC算法且缺乏这种精度时，整体系统性能可能显著削弱。本文研究了一种面向多智能体运动规划的非参数闭环行为学习方法，该方法支撑起一个数据驱动的预测控制框架。利用未知系统闭环输入/输出测量的创新方法，基于采集的数据集学习系统行为，从而构建的非参数预测模型可用于确定最优控制动作。这种非参数预测控制框架减轻了通常需要在替代方法中进行开环输入/输出测量数据采集和参数系统辨识的优化过程中的沉重计算负担。所提出的数据驱动方法也被证明能保持良好的鲁棒性。最后，通过一个多无人机系统实例展示了这一有前景方法的高效成果。