Model predictive control for distributed parameter systems using rbf neural networks
نویسندگان
چکیده
A new approach for the identification and control of distributed parameter systems is presented in this paper. A radial basis neural network is used to model the distribution of the system output variables over space and time. The neural network model is then used for synthesizing a non linear model predictive control configuration. The resulting framework is particular useful for control problems that pose constraints on the controlled variables over space. The proposed scheme is demonstrated through a tubular reactor, where the concentration and the temperature distributions are controlled using the wall temperature as the manipulated variable. The results illustrate the efficiency of the proposed methodology.
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