Gaussian Process Regression for Nonlinear Time-Varying System Identification
Daniel J. Bergmann, Michael Buchholz, Jens Niemeyer, Jörg Remele, Knut Graichen · 2018
This paper presents a method for nonlinear system identification with Gaussian process regression. The unsupervised method is able to generate an approximation of the system with correct extrapolation behaviour, that is refined with input/output-data in the typical working area and sampled online data. Therefore, an offline model is generated, which consists of a nominal model set up by the extrapolation behaviour and a detailed model for the refinement. The method is able to keep track of time-varying systems by using the confidence information to incorporate new measurements into the online model. The performance of the proposed method is tested on different numerical examples.