5 papers
Physics-informed Gaussian Processes as Linear Model Predictive Controller with Constraint Satisfaction
Jörn Tebbe, Andreas Besginow, Markus Lange-Hegermann
Model Predictive Control evolved as the state of the art paradigm for safety critical control tasks. Control-as-Inference approaches thereof model the constrained optimization prob…
Physics-informed Gaussian Processes for Model Predictive Control of Nonlinear Systems
Adrian Lepp, Jörn Tebbe, Andreas Besginow
Recently, a novel linear model predictive control algorithm based on a physics-informed Gaussian Process has been introduced, whose realizations strictly follow a system of underly…
Linear ordinary differential equations constrained Gaussian Processes for solving optimal control problems
Andreas Besginow, Markus Lange-Hegermann, Jörn Tebbe
This paper presents an intrinsic approach for addressing control problems with systems governed by linear ordinary differential equations (ODEs). We use computer algebra to constra…
Physics-informed Gaussian Processes as Linear Model Predictive Controller
Jörn Tebbe, Andreas Besginow, Markus Lange-Hegermann
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…
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
Jörn Tebbe, Christoph Zimmer, Ansgar Steland +2
Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space. Gaussian Processes (GPs) and their cali…