Physics-informed Gaussian Processes as Linear Model Predictive Controller

• URL: http://arxiv.org/abs/2412.04502v1
• Date: Mon, 02 Dec 2024 15:37:37 GMT
• Title: Physics-informed Gaussian Processes as Linear Model Predictive Controller
• Authors: Jörn Tebbe, Andreas Besginow, Markus Lange-Hegermann,
• Abstract summary: We introduce a novel algorithm for controlling linear time invariant systems in a tracking problem. The controller is based on a Gaussian Process (GP) whose realizations satisfy a system of linear ordinary differential equations with constant coefficients.
• Score: 5.89889361990138
• License:
• Abstract: We introduce a novel algorithm for controlling linear time invariant systems in a tracking problem. The controller is based on a Gaussian Process (GP) whose realizations satisfy a system of linear ordinary differential equations with constant coefficients. Control inputs for tracking are determined by conditioning the prior GP on the setpoints, i.e. control as inference. The resulting Model Predictive Control scheme incorporates pointwise soft constraints by introducing virtual setpoints to the posterior Gaussian process. We show theoretically that our controller satisfies asymptotical stability for the optimal control problem by leveraging general results from Bayesian inference and demonstrate this result in a numerical example.

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