IEEE Conference on Decision and Control 2026
Honolulu, Hawaii, USA
Paper accepted
December 15–18, 2026
Our paper “LiFT-MPC: Language-in-the-Loop Feedback Tuning of Cost Previews for MPC” (Paper 1256) has been accepted for presentation at CDC 2026 and publication in the conference proceedings.
Accepted paper · Paper 1256
LiFT-MPC: Language-in-the-Loop Feedback Tuning of Cost Previews for MPC
Abstract
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.