Predictive control of a gas–liquid separation plant based on a Gaussian process model

Abstract

Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or to its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimised. This paper demonstrates feasibility of application and realisation of a control algorithm based on a Gaussian process model. The extra information provided by the Gaussian process model is used in predictive control, where optimisation of the control signal takes the variance information into account. The feasibility of Gaussian process model usage for predictive control in industrial practice is demonstrated via the control of a gas–liquid separation plant.