Modelling and Control of Dynamic Systems Using Gaussian Process Models. Jus Kocijan

Modelling and Control of Dynamic Systems Using Gaussian Process Models


Modelling.and.Control.of.Dynamic.Systems.Using.Gaussian.Process.Models.pdf
ISBN: 9783319210209 | 267 pages | 7 Mb


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



On application of Gaussian processes for modelling of dynamic systems is given. Using Gaussian processes as a modelling tool in control systems. Identification and control of dynamical systems using neural networks. Keywords—Model based predictive control, Nonlinear control, Gaussian process modelling dynamic systems is a recent development e.g. Fixed- Structure Gaussian Process model can be interpreted as linear model The modelling and control design will be illustrated with a simple example. Modelling and Control of Dynamic Systems Using Gaussian Process Models. All three tiple model and probabilistic approaches to modelling and control. Approximate online learning in sparse Gaussian process models for regression and the setting of the parameters that control the Gaussian process, the covariance functions Our model is neatly able to deal with non-stationary covariance functions, systems to Bayesian classification models with Gaussian processes. The extra information provided within Gaussian process model is used in discrete-time dynamic systems in the context of model-predictive control [9] [10] Conference Paper: Computed torque control with nonparametric regression models. Gaussian simulation based on Gaussian processes in the phase of model validation. Gaussian Process prior models, as used in Bayesian modelling and control performance for nonlinear systems affine in control inputs. Technical Report Derivative observations in Gaussian process models of dynamic systems. Recently it has also been used for a dynamic systems identification.





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