AI 中文总结
针对MPC中时变目标成本预测难题，提出LiFT-MPC框架，通过LiFT校正方案及控制性能损失函数在线更新预测机制，经储能管理数值实验验证，可提升经济性能。
AI 中文摘要
在具有时变目标的模型预测控制（MPC）中，预测信号常需纳入成本函数，如能源系统运行中的价格。但仅根据这些信号的历史轨迹往往难以预测，因为它们可能依赖于其他上下文事件。我们提出LiFT-MPC，一个集成LiFT（语言循环反馈调优）校正方案的MPC框架，在MPC循环内完善此类预测。预测机制通过控制性能损失函数在线更新，我们为所得闭环系统建立了性能保证。使用具有实际价格和新闻背景的储能管理实际例子进行数值实验以改进预测，证明了经济性能的提升。
英文摘要
In model predictive control (MPC) with time-varying objectives, predicted signals need to be often incorporated in the cost function, such as prices in energy system operation. These are, however, often difficult to predict from the historical trajectory of these signals alone, as they may depend on other contextual events. We propose LiFT-MPC, an MPC framework that integrates a LiFT (Language-in-the-Loop Feedback Tuning) correction scheme to refine such predictions within the MPC loop. The prediction mechanism is updated online via a control-performance loss function, and we establish a performance guarantee for the resulting closed loop system. Numerical experiments using a realistic example of energy-storage management with real prices and news context to improve predictions, demonstrate an improved economic performance
