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Adaptive control of nonlinear pressure tanks using grey box modeling with neural networks

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Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
In this work, a nonlinear process is modeled in a so called 'grey box' format. This format divides the model into a known linear part and an unknown nonlinear part. The linear part is modeled using simple linear discrete dynamics whereas the nonlinear pan is modeled using a neural network with some weights. An adaptive controller is designed to incorporate the unknown nonlinear dynamics into the overall process dynamics. A smaller number of neural network weights can be used in this format so that the controller can be used adoptively for on-line control. The effectiveness of this approach is demonstrated using simulation and finally is used for real time control of a pressure tank process with excellent results.
Rocznik
Strony
45--63
Opis fizyczny
Bibliogr. 16 poz.
Twórcy
autor
  • Departament of Chemical & Biochemical Engineering, The University of Western Ontario, London Ontario, N6A 5B8, Canada
autor
  • Departament of Chemical & Biochemical Engineering, The University of Western Ontario, London Ontario, N6A 5B8, Canada
Bibliografia
  • [1] Bhat, N.V. and TJ. McAvoy, 'Use of Neural Nets for Dynamical Modeling and Control of Chemical Process Systems', Computers Chem. Eng. 14, 573-583,1990.
  • [2] Bose, N. K. and Liang, P. 'Neural Network Fundamentals with Graphs', Algorithms and Applications, McGraw-Hill, Inc., New York, 1996.
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  • [4] [4] Chai, T. Y., An indirect Stochastic Adaptive Scheme with on-line Choice of Weightings Polynomials, IEEE Trans. Auto. Contr. AC-34,1990.
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  • [9] Funahashi K., 'On the Approximate Realization of Continuous Mappings by Neural Networks’, Vol.2, 183-192, 1989.
  • [10] Goldberg, K. and B. A. Pearlmutter,'Using a Neural Networks to learn the dynamic of the CMU Direct-drive Arm 1Г, Report CMU-CS-88-160,Camegie-Mellon University, 1988.
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  • [13] Hecht-Nielsen R.,'Theory of the Back-Propagation Neural Networks', Proc.IEEE, Int. Conf. Neural Networks, 1-593-605, 1989.
  • [14] Hoenik K.,M.Stinchcombe and H. White, 'Multilayer Feedforward Networks are Universal Approxmators', Neural Networks, Vol.2, 359-366, 1989
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  • [16] Zhu Q.M., K. Warwick and J.L.Douce,'Adaptive General Prodictive Controller for Nonlinear System1,IEE, Proc. D, Vol. 138, 33-40,1991.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-LOD7-0028-0047
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