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This paper describes a nonlinear Model Predictive Control (MPC) algorithm for a distributed parameter thermal system (a long duct). For prediction a specially designed neural model of the process is used. The model consists of a set of local neural sub-models, which calculate temperatures for a number of predefined locations of sensors, and a neural interpolator, which calculates the temperature for...
This paper is concerned with black-box modelling of a distributed parameter thermal system (a long duct) by means of neural networks. A new model structure is discussed which consists of a set of local neural sub-models and a neural interpolator. The local sub-models calculate temperatures for a number of predefined locations of sensors. They are trained independently, using limited data sets. Next,...
This paper describes a Model Predictive Control (MPC) algorithm in which a Radial Basis Function (RBF) neural network is used as a dynamic model of the controlled process and it reports training and selection of the RBF model of the benchmark system for MPC. In order to obtain a computationally uncomplicated control scheme, the RBF model is successively linearised on-line, which leads to an easy to...
This paper discusses the possibility of using a Jordan neural network as a model of dynamic systems and it presents a Model Predictive Control (MPC) algorithm in which such a network is used for prediction. The Jordan network is a simple recurrent neural structure in which only one value of the process input signal (from the previous sampling instant) and only one value of the delayed output signal...
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