Research theme

Safe and distributed control

How can a controller learn while preserving reliable operation?

Predictive control with performance and feasibility guarantees. The work spans perturbation analysis, nonlinear policy certification, aggregate flexibility, and distributed system level synthesis.

Explore the research map

Papers

2026IEEE Control Systems Letters

VigilMPC: Certified Online Tube Model Predictive Control for Safe Real-Time Neuromodulation

Yu Mao, Zhichao Liang, Junxiang Zhang, Quanying Liu, Tongxin Li

VigilMPC accepts a predictor update only when a backup plan remains feasible under tightened constraints. Recursive feasibility and constraint satisfaction follow under the tube MPC assumptions. Evaluation uses simulated neural dynamics and a switching benchmark, rather than clinical deployment.

Control Predictions Paper
Can a predictive controller safely update its learned dynamics online? model predictive control MPC stability recursive feasibility distributed control system level synthesis learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2025CDC

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

Ruixiang Wu, Jiahao Ai, Tongxin Li

A language to distribution module converts contextual instructions into disturbance predictions for MPC. The framework closes the loop between instructions, predictions, and control, with a regret analysis for linear dynamics under the stated training assumptions.

Agents Control Energy Paper
How can human instructions improve predictive control? large language models LLM agents contextual control world models reinforcement learning dueling bandits model predictive control MPC stability recursive feasibility distributed control system level synthesis AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2025ACM e-Energy

Learning-Augmented Online Control for Decarbonizing Water Infrastructures

Jianyi Yang, Pengfei Li, Tongxin Li, Adam Wierman, Shaolei Ren

LAOC restricts online decisions to safe action sets while exploiting learned predictions for pump scheduling. Analysis characterizes the relationship between safety requirements and operating costs, with a building water supply case study.

Energy Control Predictions Paper
Can learned pump control reduce carbon costs while meeting safety constraints? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization model predictive control MPC stability recursive feasibility distributed control system level synthesis learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2025ACM e-Energy demo

Open In-Context Energy Management Platform

Yikai Lu, Tinko Sebastian Bartels, Ruixiang Wu, Fanzeng Xia, Xudong Wang, Yifei Wu, Haoxiang Yang, Tongxin Li

OpenCEM presents a platform design connecting energy time series, events, human context, and simulation. An on campus solar and battery installation motivates evaluation of context sensitive control. The paper describes planned data and API capabilities.

Agents Energy Control Paper
What would a shared benchmark for contextual energy management provide? large language models LLM agents contextual control world models reinforcement learning dueling bandits AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2025IEEE Transactions on Transportation Electrification

Out-of-Distribution-Aware Electric Vehicle Charging

Tongxin Li, Chenxi Sun

OOD Charging adapts an awareness radius using temporal difference errors to balance learned scheduling with a robust baseline. Evaluation on ACN data examines behavioral changes during COVID 19 and the resulting distribution shift.

Energy Predictions Control Paper
How should charging schedules respond when user behavior changes? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2025CDC

PredSLS: A System-Level Framework for Distributed Predictive Control

Yifei Wu, Jing Yu, Tongxin Li

PredSLS integrates forecasts directly into system level synthesis with locality constraints. Temporal decay of prediction effects supports finite horizon approximations, while spatial and temporal decomposition enable scalable computation across a network.

Control Predictions Paper
How can distributed controllers use predictions with limited communication? model predictive control MPC stability recursive feasibility distributed control system level synthesis learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2025Environmental Data Science

Robust machine-learned algorithms for efficient grid operation

Nicolas Christianson, Christopher Yeh, Tongxin Li, Mehdi Hosseini, Mahdi Torabi Rad, Azarang Golmohammadi, Adam Wierman

RobustML combines a learned dispatch algorithm with a bound on worst case cost. A combined cycle cogeneration case study examines dispatch and ramping costs as renewable penetration increases, including performance under distribution shift.

Energy Predictions Control Paper
Can learned dispatch policies remain reliable under distribution shift? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2024NeurIPS

Disentangling Linear Quadratic Control with Untrusted ML Predictions

Tongxin Li, Hao Liu, Yisong Yue

DISC learns confidence in predictions of latent disturbance components. Competitive analysis covers linear and more general mixing functions, showing how online confidence adaptation can exploit accurate forecasts while maintaining protection against large errors.

Predictions Control Paper
Can a controller learn which components of a forecast to trust? learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2023NeurIPS

Anytime-Competitive Reinforcement Learning with Policy Prior

Jianyi Yang, Pengfei Li, Tongxin Li, Adam Wierman, Shaolei Ren

ACRL constrains cumulative costs relative to a policy prior at each round, rather than only in expectation over episodes. The analysis establishes cost guarantees and regret relative to the constrained optimum, with experiments in carbon aware computing.

Agents Predictions Control Paper
Can reinforcement learning enforce cost protection throughout an episode? large language models LLM agents contextual control world models reinforcement learning dueling bandits learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2023IEEE Open Journal of Control Systems

Certifying Black-Box Policies With Stability for Nonlinear Control

Tongxin Li, Ruixiao Yang, Guannan Qu, Yiheng Lin, Adam Wierman, Steven H. Low

Simply blending two stabilizing policies can cause instability. An adaptive confidence policy instead uses approximate model information to certify stability, with competitive guarantees under bounded nonlinearity and evaluations on Cart Pole and charging under distribution shift.

Control Predictions Energy Paper
How can model based advice stabilize a learned nonlinear policy? model predictive control MPC stability recursive feasibility distributed control system level synthesis learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2022NeurIPS

Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity

Yiheng Lin, Yang Hu, Guannan Qu, Tongxin Li, Adam Wierman

A general analysis pipeline converts perturbation bounds for finite horizon control into dynamic regret bounds for MPC. It handles prediction errors in costs, dynamics, and disturbances, and extends the analysis to constrained and nonlinear settings under suitable regularity conditions.

Control Predictions Paper
How do forecast errors translate into regret for MPC? model predictive control MPC stability recursive feasibility distributed control system level synthesis learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization
2022POMACS

Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions

Tongxin Li, Ruixiao Yang, Guannan Qu, Guanya Shi, Chenkai Yu, Adam Wierman, Steven Low

A confidence parameter governs how much a linear quadratic controller trusts disturbance predictions. Competitive bounds describe the tradeoff between accurate and inaccurate advice, and a self tuning policy adapts confidence online using observed prediction quality.

Predictions Control Paper
Can predictive control balance consistency and robustness automatically? learning augmented algorithms algorithms with predictions competitive analysis robustness consistency online optimization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2022Electric Power Systems Research

Towards balanced three-phase charging: Phase optimization in adaptive charging networks

Zixin Ye, Tongxin Li, Steven Low

PXA solves a relaxation of the phase optimization problem, with convergence guarantees under specified conditions. Its MPC implementation updates decisions online. Experiments use adaptive charging network data to examine energy delivery, charging costs, and phase balance.

Energy Control Paper
How should electric vehicle demand be assigned across electrical phases? AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization model predictive control MPC stability recursive feasibility distributed control system level synthesis
2021POMACS

Information Aggregation for Constrained Online Control

Tongxin Li, Yue Chen, Bo Sun, Adam Wierman, Steven Low

Entropic aggregation summarizes a local controller's feasible actions for a remote decision maker. Penalized Predictive Control can approach the regret of full information control under causal invariance and a sufficiently long prediction window.

Control Information Energy Paper
How much feasibility information must a local controller share? model predictive control MPC stability recursive feasibility distributed control system level synthesis information theory graph learning sample complexity compressed sensing graph neural networks Riemannian geometry AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization
2021IEEE Transactions on Smart Grid

Learning-Based Predictive Control via Real-Time Aggregate Flexibility

Tongxin Li, Bo Sun, Yue Chen, Zixin Ye, Steven H. Low, Adam Wierman

Maximum entropy feedback summarizes the feasible actions of controllable loads. Reinforcement learning approximates this feedback for Penalized Predictive Control, reducing information and computation requirements. Charging data demonstrates the coordination approach.

Control Energy Agents Paper
How can an aggregator communicate flexibility in real time? model predictive control MPC stability recursive feasibility distributed control system level synthesis AI for energy electric vehicle charging demand response renewable energy load forecasting decarbonization large language models LLM agents contextual control world models reinforcement learning dueling bandits

Connected themes

From prediction error to control performance

Trust adaptation in linear quadratic control and perturbation bounds for MPC connect the quality of a forecast to the cost of acting on it. Nonlinear certification adds stability to this picture.

From language context to constrained actions

INSTRUCT MPC translates human instructions into disturbance predictions. World model certification and VigilMPC study complementary ways of deciding which learned models can support reliable planning and safe updates.

Guarantees meet physical infrastructure

Aggregate flexibility communicates feasible actions for coordinated charging. Robust dispatch and safe water control connect performance guarantees to operational costs and physical constraints.

Open directions

Certified contextual control

Can contextual and world model updates be certified before they affect a physical system?

Connect language conditioned predictions with local world model guarantees and feasibility checks. An open challenge is preserving safety when both the context and the learned dynamics change during operation.

Coordinated low carbon infrastructure

Can distributed energy and water resources adapt together under changing conditions?

Combine local feasibility summaries with distributed predictive control and contextual energy models. Open questions include communication limits, distribution shift, and guarantees across coupled physical networks.