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ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
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ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
A novel fuzzy compensation multi-class support vector machine
Applied Intelligence
Fuzzy multiple modeling and fuzzy predictive control of a tubular heat exchanger system
AEE'08 Proceedings of the 7th WSEAS International Conference on Application of Electrical Engineering
Multiple modeling and fuzzy predictive control of a tubular heat exchanger system
WSEAS Transactions on Systems and Control
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IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
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Model predictive control using fuzzy decision functions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The purpose of this paper is to deal with a novel intelligent predictive control scheme using the multiple models strategy with its application to an industrial tubular heat exchanger system. The main idea of the strategy proposed here is to represent the operating environments of the system, which have a wide range of variation in the span of time by several local explicit linear models. In line with this strategy, the well-known linear generalized predictive control (LGPC) schemes are initially designed corresponding to each one of the linear models of the system. After that, the best model of the system and the LGPC control action are precisely identified, at each instant of time, by an intelligent decision maker scheme (IDMS), which is playing the so important role in realizing the finalized control action for the system. In such a case, as soon as each model could be identified as the best model, the adaptive algorithm is implemented on the both chosen model and the corresponding predictive control schemes. In conclusion, for having a good tracking performance, the predictive control action is instantly updated and is also applied to the system, at each instant of time. In order to demonstrate the effectiveness of the proposed approach, simulations are carried out and the results are compared with those obtained using a nonlinear GPC (NLGPC) scheme as a benchmark approach realized based on the Wiener model of the system. In agreement with these results, the validity of the proposed control scheme can tangibly be verified.