AI RESEARCH
A robust and adaptive MPC formulation for Gaussian process models
arXiv CS.LG
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ArXi:2507.02098v2 Announce Type: replace-cross In this paper, we present a robust and adaptive model predictive control (MPC) framework for uncertain nonlinear systems affected by bounded disturbances and unmodeled nonlinearities. We use Gaussian Processes (GPs) to learn the uncertain dynamics based on noisy measurements, including those collected during system operation. As a key contribution, we derive robust predictions for GP models using contraction metrics, which are incorporated in the MPC formulation.