Model Predictive Control (MPC) is a powerful framework for optimal control but can be too slow for low-latency applications. We present a data-driven framework to accelerate MPC by replacing online optimization with a nonparametric policy constructed from offline MPC solutions. Our policy is greedy with respect to a constructed upper bound on the optimal cost-to-go, and can be implemented as a nonparametric lookup rule that is orders of magnitude faster than solving MPC online. Our analysis shows that under sufficient coverage condition of the offline data, the policy is recursively feasible and admits provable, bounded optimality gap. These conditions establish an explicit trade-off between the amount of data collected and the tightness of the bounds. Our experiments show that this policy is between 100 and 1000 times faster than standard MPC, with only a modest hit to optimality, showing potential for real-time control tasks.

翻译：模型预测控制（MPC）是一种强大的最优控制框架，但在低延迟应用中可能因计算速度过慢而受限。本文提出一种数据驱动框架，通过使用离线MPC解构建的非参数化策略替代在线优化过程，从而加速MPC。该策略基于构建的最优代价函数上界采用贪婪策略，可实现为非参数化查找规则，其计算速度比在线求解MPC快数个数量级。理论分析表明，在离线数据满足充分覆盖条件时，该策略具有递归可行性，并可证明存在有界的最优性差距。这些条件明确了数据收集量与边界紧致性之间的显式权衡关系。实验结果表明，该策略相比标准MPC提速100至1000倍，且最优性损失较小，展现了其在实时控制任务中的应用潜力。