EXPLICIT MODEL PREDICTIVE CONTROL OF GAS-LIQUID SEPARATION PLANT (2007)
A. Grancharova, T. A. Johansen, J. Kocijan
Exact or approximate solutions to constrained linear model predictive control problems can be pre-computed off-line in an explicit form as a piecewise linear state feedback defined on a polyhedral...
Abstract Computational Intelligence in Wastewater Treatment (2007)
A. Stathaki, R. E. King, N. Hvala, J. Kocijan
In this paper we propose a novel intelligent control scheme that involves a cluster of Agents embedded in an Intelligent Co-ordinator. The resultant Intelligent Co-ordinator can be added to any...
Nonlinear predictive control with a gaussian process model (2005)
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...
Gaussian process model based predictive control (2004)
Kocijan,J., Murray-Smith,R., Rasmussen,C.E., Girard,A.
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identi cation of non-linear dynamic systems. The Gaussian processes can highlight areas of the input...
Gaussian process model based predictive control (2004)
Kocijan, J., Murray-Smith, R., Rasmussen, C.E., Girard, A.
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identi cation of non-linear dynamic systems. The Gaussian processes can highlight areas of the input...
Gaussian process model based predictive control (2004)
Kocijan, J., Murray-Smith, R., Rasmussen, C.E., Girard, A.
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...
A case based comparison of identification with neural network and Gaussian process models. (2003)
Kocijan,J., Banko,B., Likar,B., Girard,A., Murray-Smith,R., Rasmussen,C.E.
In this paper an alternative approach to black-box identification of non-linear dynamic systems is compared with the more established approach of using artificial neural networks. The Gaussian...
Predictive control with Gaussian process models (2003)
Kocijan,J., Murray-Smith,R., Rasmussen,C.E., Likar,B.
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...
Predictive control with Gaussian process models (2003)
Kocijan, J., Murray-Smith, R., Rasmussen, C.E., Likar, B., Zajc, B., Tkal, M.
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...
A case based comparison of identification with neural network and Gaussian process models. (2003)
Kocijan, J., Banko, B., Likar, B., Girard, A., Murray-Smith, R., Rasmussen, C.E., ...
In this paper an alternative approach to black-box identification of non-linear dynamic systems is compared with the more established approach of using artificial neural networks. The Gaussian...
Gaussian process model based predictive control
Kocijan, J., Murray-Smith, R., Rasmussen, C.E., Girard, A.
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...
Gaussian process model based predictive control
Kocijan, J., Murray-Smith, R., Rasmussen, C.E., Girard, A.
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...
Nonlinear predictive control with a gaussian process model
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...
Nonlinear predictive control with a gaussian process model
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...