A robust and adaptive MPC formulation for Gaussian process models

arXiv:2507.02098 · eess.SY, cs.LG, cs.SY, math.OC · Submitted 2025-07-02 · Read on arXiv

eess.SY, cs.LG, cs.SY, math.OC

Submitted: 2025-07-02

Updated: 2026-09-04

Comments: This is the accepted version of the paper in Automatica, 2026. The code is available: https://doi.org/10.3929/ethz-c-000803178

Journal ref: Automatica (2026)

DOI: 10.1016/j.automatica.2026.113276

Code: https://github.com/Gedlex/nonlinear-robust-MPC

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Lyapunov function — 4× in this paper · explained in 2 episodes
• Model Predictive Control (MPC) — 4× in this paper · explained in 5 episodes
• Theoretical Guarantees — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Gaussian Processes (GPs) — 3× in this paper · explained in 2 episodes
• Hilbert Space — 2× in this paper · explained in 2 episodes
• American Control Conference — 1× in this paper
• Bayesian Optimization — 1× in this paper · explained in 3 episodes
• Closed-Loop System — 1× in this paper · explained in 4 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Initial Data — 1× in this paper · explained in 2 episodes
• Online Adaptation — 1× in this paper · explained in 3 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• Optimal kernel regression bounds under energy-bounded noise
• Stochastic Model Predictive Control for Sub-Gaussian Noise
• Towards safe Bayesian optimization with Wiener kernel regression
• Finite-Sample-Based Reachability for Safe Control with Gaussian Process Dynamics

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