1.

Dynamical systems identification using Gaussian process models with incorporated local models

The paper describes the validation of the method for identification of Gaussian process model with incorporated prior knowledge in the form of local linear dynamic models. These are the first ever published results of Gaussian process model with incorporated local models method used to identify a higher order system and also the first results where the method was used to identify a system using measurement data.

COBISS.SI-ID: 24397095

2.

Explicit output-feedback nonlinear predictive control based on black-box models

This paper describes an approximate multi-parametric nonlinear programming approach to explicitly solve output-feedback NMPC problems for constrained nonlinear systems described by black-box models like for example artificial neural networks or Gaussian process models. A dual-mode control strategy is employed in order to achieve an offset-free closed-loop response in the presence of bounded disturbances and/or model errors.

COBISS.SI-ID: 24397351

3.

On-line Gaussian process model for the prediction of the ozone concentration in the air

On-line Gaussian process modelling for the prediction of the ozone pollution in the air; Description: On-line Gaussian process modelling method has been implemented for the study of the ozone pollution in the air. The selected method is suitable in particular because it adapts model according to streaming measurement data to the complex dynamics and not entirely known mechanism in the background of ozone generation.

COBISS.SI-ID: 24443431