Real-Time Online Learning for Model Predictive Control using a Spatio-Temporal Gaussian Process Approximation

• URL: http://arxiv.org/abs/2603.17632v1
• Date: Wed, 18 Mar 2026 11:53:21 GMT
• Title: Real-Time Online Learning for Model Predictive Control using a Spatio-Temporal Gaussian Process Approximation
• Abstract summary: This work presents an efficient implementation of an approximatetemporal GP model, offering online learning at constant computational complexity.<n>It is optimized for GP-MPC, where it enables improved control performance by learning more accurate system dynamics online in real-time.<n>The performance of the proposed method is demonstrated by simulations and hardware experiments in the exemplary application of autonomous miniature racing.
• Score: 3.1977149161426657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning-based model predictive control (MPC) can enhance control performance by correcting for model inaccuracies, enabling more precise state trajectory predictions than traditional MPC. A common approach is to model unknown residual dynamics as a Gaussian process (GP), which leverages data and also provides an estimate of the associated uncertainty. However, the high computational cost of online learning poses a major challenge for real-time GP-MPC applications. This work presents an efficient implementation of an approximate spatio-temporal GP model, offering online learning at constant computational complexity. It is optimized for GP-MPC, where it enables improved control performance by learning more accurate system dynamics online in real-time, even for time-varying systems. The performance of the proposed method is demonstrated by simulations and hardware experiments in the exemplary application of autonomous miniature racing.

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