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— 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 its complexity, by indicating the higher variance around the predicted mean. Gaussian(More)
—This paper describes model-based predictive control based on Gaussian processes. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. It offers more insight in variance of obtained model response , as well as fewer parameters to determine than other models. The(More)
Gaussian process (GP) models are non-parametric, black-box models that represent a new method for system identification. The optimization of GP models, due to their probabilistic nature, is based on maximization of the probability of the model. This probability can be calculated by the marginal likelihood. Commonly used approaches for maximizing the(More)
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear 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(More)
Gaussian process models provide a probabilistic non-para-metric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process(More)
Solutions to constrained linear model predictive control (MPC) problems can be pre-computed off-line in an explicit form as a piecewise linear (PWL) state feedback defined on a polyhedral partition of the state space. This admits implementation at high sampling frequencies in real-time systems with high reliability and low software complexity. Recently,(More)
The traditional model-based fault detection and isolation (FDI) rely on tacit assumption that the validity of process model is out of question for current process data. However, it happens in practice that a process might end up in regions for which the underlying model is not validated. This can result in false alarms. To avoid the risk, a validity index(More)