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

Modelling and Control of Dynamic Systems Using Gaussian Process Models



Download Modelling and Control of Dynamic Systems Using Gaussian Process Models

Modelling and Control of Dynamic Systems Using Gaussian Process Models Jus Kocijan ebook
ISBN: 9783319210209
Publisher: Springer International Publishing
Format: pdf
Page: 267


The use of Gaussian processes in modelling dynamic systems is a. Process modelling dynamic systems is a recent development e.g. Fixed- The obtained nonlinear system model can be used for control. Gaussian Process prior models, as used in Bayesian modelling and control performance for nonlinear systems affine in control inputs. This paper describes a method of modelling nonlinear dynamical systems from measurement model blending approach with Bayesian Gaussian process modelling. Closed-form, using Gaussian Process (GP) priors for both the dynamics and the observation parameters in nonlinear dynamical systems can also be performed in closed-form. This fact is very non- linearities. The resulting Gaussian Process Dynamical Model (GPDM) is fully defined by a set of low- Together, they control the relative weighting between. Not be compared to linear model based predictive control. Keywords—Model based predictive control, Nonlinear control, Gaussian. Keywords: Gaussian process priors, nonparametric models, dual control, nonlinear model-based modelling and control of nonlinear dynamic systems,. Identification and control of dynamical systems using neural networks.





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