[论文解读] Data-Driven Predictive Control for Multi-Agent Decision Making With Chance Constraints.

Jun Ma, Zilong Cheng+4 more|arXiv (Cornell University)|Nov 6, 2020

Control Systems and Identification参考文献 2被引用 9

一句话总结

本文提出了一种基于非参数模型的数据驱动预测控制框架，利用满足持久激励条件的闭环输入/输出数据，实现高效且鲁棒的MPC，同时满足概率约束。通过用数据驱动建模替代计算量大的参数系统辨识，该方法在多架无人机系统中实现了卓越性能。

ABSTRACT

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.

研究动机与目标

• 解决传统模型预测控制（MPC）在多智能体系统中因系统辨识不准确带来的挑战。
• 克服传统MPC中参数系统辨识与开环数据采集带来的高计算负担。
• 利用满足持久激励条件的闭环测量数据，构建非参数预测模型。
• 通过概率约束的保守近似，将随机MPC问题转化为确定性优化问题。
• 在提升计算效率与系统性能的同时，保持鲁棒性，适用于多智能体决策。

提出的方法

• 基于满足持久激励条件的闭环输入/输出测量数据，构建非参数预测模型。
• 用数据驱动建模方法替代传统参数系统辨识，以降低计算复杂度。
• 应用保守近似方法处理概率约束，将随机MPC问题转化为确定性问题。
• 高效地构建并求解确定性优化问题，实现多智能体系统中的实时应用。
• 通过数据驱动模型的结构与保守约束近似，确保系统鲁棒性。
• 通过多架无人机系统验证该方法，证明其在实际应用中的可行性与性能。

实验结果

研究问题

• RQ1 数据驱动的预测控制方法是否能在不牺牲性能的前提下，显著降低多智能体MPC的计算负担？
• RQ2 在持久激励条件下使用闭环数据，如何提升多智能体系统中模型的准确性和鲁棒性？
• RQ3 对概率约束进行保守近似，在保证系统安全的同时，能在多大程度上实现高效优化？
• RQ4 所提出的方法是否能在真实世界多智能体应用（如无人机编队）中实现可靠性能？
• RQ5 与传统参数辨识方法相比，该数据驱动模型在计算成本与鲁棒性方面表现如何？

主要发现

• 该数据驱动方法显著降低了MPC中与参数系统辨识相关的计算负担。
• 基于闭环数据构建的非参数预测模型，无需显式参数假设，即可实现对系统的有效表征。
• 对概率约束的保守近似，使得MPC问题能够高效求解，同时保持概率安全保证。
• 即使在系统辨识不完善的情况下，该方法仍保持了强鲁棒性。
• 该方法在多架无人机系统中表现出高度的实际有效性，在真实场景验证中实现了良好的系统性能。
• 该框架通过避免昂贵的开环数据采集与参数拟合，实现了多智能体系统中高效、实时的控制。