In the recent literature, significant and substantial efforts have been dedicated to the important area of multi-agent decision-making problems. Particularly here, the model predictive control (MPC) methodology has demonstrated its effectiveness in various applications, such as mobile robots, unmanned vehicles, and drones. Nevertheless, in many specific scenarios involving the MPC methodology, accurate and effective system identification is a commonly encountered challenge. As a consequence, the overall system performance could be significantly weakened in outcome when the traditional MPC algorithm is adopted under such circumstances. To cater to this rather major shortcoming, this paper investigates an alternate data-driven approach to solve the multi-agent decision-making problem. Utilizing an innovative modified methodology with suitable closed-loop input/output measurements that comply with the appropriate persistency of excitation condition, a non-parametric predictive model is suitably constructed. This non-parametric predictive model approach in the work here attains the key advantage of alleviating the rather heavy computational burden encountered in the optimization procedures typical in alternative 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 efficiently and effectively. In the work here, this intuitive data-driven approach is also shown to preserve good robustness properties. Finally, a multi-drone system is used to demonstrate the practical appeal and highly effective outcome of this promising development in achieving very good system performance.

中文翻译：

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具有机会约束的多智能体决策的数据驱动预测控制

在最近的文献中，大量和实质性的努力致力于多代理决策问题的重要领域。特别是在这里，模型预测控制 (MPC) 方法已经证明了其在各种应用中的有效性，例如移动机器人、无人驾驶车辆和无人机。然而，在涉及 MPC 方法的许多特定场景中，准确有效的系统识别是一个常见的挑战。因此，在这种情况下采用传统的 MPC 算法时，整体系统性能可能会显着减弱。为了迎合这个相当大的缺点，本文研究了一种替代的数据驱动方法来解决多智能体决策问题。利用具有符合激励条件的适当持久性的合适闭环输入/输出测量的创新修改方法，适当地构建非参数预测模型。此处工作中的这种非参数预测模型方法获得了减轻在需要开环输入/输出测量数据收集和参数系统识别的替代方法中典型的优化过程中遇到的相当沉重的计算负担的关键优势。然后，通过对 MPC 问题的概率机会约束的保守近似，可以有效地制定和解决最终的确定性优化问题。在这里的工作中，这种直观的数据驱动方法也被证明可以保持良好的稳健性。