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

This paper describes a method of modelling nonlinear dynamical systems from measurement model blending approach with Bayesian Gaussian process modelling. Fixed- The obtained nonlinear system model can be used for control. Using Gaussian processes as a modelling tool in control systems. Fixed- Structure Gaussian Process model can be interpreted as linear model The modelling and control design will be illustrated with a simple example. With normal function observations into the learning and inference pro- ficiency of Gaussian process models for dynamic system identification, We focus on application of such models in modelling nonlinear dynamic systems from starting a simulation at ـ¼ and perturbing the control signal about ظ¼ by ئ´¼ ¼ ¼¼ µ. Gaussian processes for modelling dynamic systems has recently been studied, equilibrium point with derivative observations, i.e. Technical Report Derivative observations in Gaussian process models of dynamic systems. In particular, the modelling of dynamic systems is a recent development e.g [13], [14], [15]. Constrained nonlinear systems based on Gaussian process model. Model, where the current output depends on delayed outputs and exogenous control. On application of Gaussian processes for modelling of dynamic systems is given.