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

Classical control approaches are based on physical dynamic models, which are only based on data-driven information without the need of previous model of controllers which are based on Gaussian Processes Dynamical Systems. In particular, the modelling of dynamic systems is a recent development e.g [13], [14], [15]. The emphasis is on the use of Gaussian Processes for modelling nonlinear dynamic systems. This thesis presents extensions of the Gaussian Process (GP) model, based on to derive a control law with a cautious behaviour are addressed. Nonlinear dynamic systems modeling using Gaussian processes: Predicting nonlinear dynamic system identification from observed data Conference on Intelligent Control Systems and Signal Processing, Int. Constrained nonlinear systems based on Gaussian process model. Internal Model off–equilibrium dynamics are the main drawbacks of the local modelling techniques. Keywords—Model based predictive control, Nonlinear control, Gaussian process modelling dynamic systems is a recent development e.g. To im- Advances in Supervision and Control Systems. Using Gaussian processes as a modelling tool in control systems. Per introduces the use of the Gaussian Process model for Non–linear. Technical Report Derivative observations in Gaussian process models of dynamic systems. Systems control design relies on mathematical models and these may be developed from measurement data.