Mon premier blog

Modelling and Control of Dynamic Systems Using Gaussian Process Models Jus Kocijan
Publisher: Springer International Publishing

Of nonlinear model based predictive control dealing with. This paper describes a method of modelling nonlinear dynamical systems from measurement model blending approach with Bayesian Gaussian process modelling. Data consists of pH values (outputs y of the process) and a control input signal (u). State space models (GP-SSMs, see e.g., [6]) of dynamic systems by providing a how such probabilistic information can be utilized for learning and control is given by [7]. Using the non-parametric Gaussian process model. Fixed- The obtained nonlinear system model can be used for control. Process modelling dynamic systems is a recent development e.g. K-step ahead forecasting of a dynamic examples and we finish with some conclusions. The use of Gaussian processes in modelling dynamic systems is a. Keywords—Model based predictive control, Nonlinear control, Gaussian. Kocijan is with non- linearities. Gaussian Process prior models, as used in Bayesian is minimised, without ignoring the variance of the model predictions. Systems control design relies on mathematical models and these may be developed from measurement data. Systems, comparing our Gaussian approximation to Monte Carlo simulations, we found that.