关键词：非线性行为；高斯工程；控制器
摘 要：Many engineering systems can be characterized as complex since they have a nonlinear behaviour incorporating a stochastic uncertainty. It has been shown that one of the most appropriate methods for modelling of such systems is based on the application of Gaussian processes (GPs). The GP models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear stochastic systems. This chapter reviews the methods for modelling and control of complex stochastic systems based on GP models. The GP-based modelling method is applied in a process engineering case study, which represents the dynamic modelling and control of a laboratory gas-liquid separator. The variables to be controlled are the pressure and the liquid level in the separator and the manipulated variables are the apertures of the valves for the gas flow and the liquid flow. GP models with different regressors and different covariance functions are obtained and evaluated. A selected GP model of the gas-liquid separator is further used to design an explicit stochastic model predictive controller to ensure the optimal control of the separator.