arXiv subjects

Jiahao Ai

Publications and source records attributed to Jiahao Ai.

3 recordsLinked to original sources

arXiv2026

Context-Aware Model Predictive Control for Microgrid Energy Management via LLMs

The optimal operation of modern microgrids, particularly those integrating stochastic renewable generation and battery energy storage system (BESS), relies heavily on load and disturbances forecasting to minimize operational costs. However, in environments with uncertainties in both generation and consumption, traditional numerical forecasting methods often fail to capture generation shifts and event-driven load surges. While contextual information regarding event schedules, system logs, and computational task records is easily obtainable, classic control paradigms lack a formal interface to integrate the unstructured, semantic data into the physical operation loop. This paper addresses this gap by introducing the InstructMPC framework, which utilizes a Large Language Model (LLM) paired with a tunable last layer mapping to translate unstructured operational context into predictive disturbance trajectories for the MPC controller. Unlike conventional forecasting methods, the proposed approach treats the last layer mapping as a tunable component, refined online based on the realized control cost. We establish a theoretical foundation for this closed-loop tuning strategy, proving a regret bound of $O(\sqrt{T \log T})$ for linear systems under a tailored task-aware loss function, together with robustness guarantees against uninformative or noisy textual inputs. The control strategy is experimentally validated on OpenCEM, a real-world microgrid with highly fluctuating generation and consumption. Experimental results demonstrate that the LLM-driven MPC significantly reduces cumulative grid electricity costs compared to classical context-agnostic baselines, validating the efficacy of integrating semantic information directly into physical control loops.

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arXiv2025

InstructMPC: A Human-LLM-in-the-Loop Framework for Context-Aware Control

Model Predictive Control (MPC) is a powerful control strategy widely utilized in domains like energy management, building control, and autonomous systems. However, its effectiveness in real-world settings is challenged by the need to incorporate context-specific predictions and expert instructions, which traditional MPC often neglects. We propose InstructMPC, a novel framework that addresses this gap by integrating real-time human instructions through a Large Language Model (LLM) to produce context-aware predictions for MPC. Our method employs a Language-to-Distribution (L2D) module to translate contextual information into predictive disturbance trajectories, which are then incorporated into the MPC optimization. Unlike existing context-aware and language-based MPC models, InstructMPC enables dynamic human-LLM interaction and fine-tunes the L2D module in a closed loop with theoretical performance guarantees, achieving a regret bound of $O(\sqrt{T\log T})$ for linear dynamics when optimized via advanced fine-tuning methods such as Direct Preference Optimization (DPO) using a tailored loss function.

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arXiv2025

Not all distributional shifts are equal: Fine-grained robust conformal inference

We introduce a fine-grained framework for uncertainty quantification of predictive models under distributional shifts. This framework distinguishes the shift in covariate distributions from that in the conditional relationship between the outcome ($Y$) and the covariates ($X$). We propose to reweight the training samples to adjust for an identifiable covariate shift while protecting against worst-case conditional distribution shift bounded in an $f$-divergence ball. Based on ideas from conformal inference and distributionally robust learning, we present an algorithm that outputs (approximately) valid and efficient prediction intervals in the presence of distributional shifts. As a use case, we apply the framework to sensitivity analysis of individual treatment effects with hidden confounding. The proposed methods are evaluated in simulation studies and four real data applications, demonstrating superior robustness and efficiency compared with existing benchmarks.

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