Derivative observations used in predictive control

Gaussian processes provide approach to probabilistic nonparametric modelling which allows a straightforward combination of measured data and local linear models in an empirical model. This is of particular importance in the identification of nonlinear dynamic systems from experimental data where usually more data are available far from equilibrium points. We illustrate the utility of such simple nonlinear predictive control example.