IEEE Control Systems Letters · 2026

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

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

IEEE Control Systems Letters

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Can a predictive controller safely update its learned dynamics online?

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.

Research themes

Connections

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.

Open questions

Certified contextual control

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

Explore open directions

Cite this paper

Yu Mao, Zhichao Liang, Junxiang Zhang, Quanying Liu, Tongxin Li. VigilMPC: Certified Online Tube Model Predictive Control for Safe Real-Time Neuromodulation. IEEE Control Systems Letters, 2026. https://doi.org/10.1109/LCSYS.2026.3711599

BibTeX
@article{tongxin-vigilmpc,
  title = {{VigilMPC: Certified Online Tube Model Predictive Control for Safe Real-Time Neuromodulation}},
  author = {Yu Mao and Zhichao Liang and Junxiang Zhang and Quanying Liu and Tongxin Li},
  year = {2026},
  journal = {IEEE Control Systems Letters},
  url = {https://doi.org/10.1109/LCSYS.2026.3711599},
  doi = {10.1109/LCSYS.2026.3711599}
}