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MIMO Fuzzy Logic Control of a Liquid Level Process

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Artificial Neural Nets and Genetic Algorithms
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Abstract

A large number of design strategies exist for multivariable control situations. Many of the methods require a linear time-invariant process characterisation in the form of a state space model or transfer function matrix description. Quite often this is not available and could be expensive to realise. If this latter route is pursued there needs to be considerable benefits in the quality of the resulting closed-loop performance. One alternative is to use the ‘expert’ approach of fuzzy logic where the plant is not modelled but the expert operator is. Application of such a controller is not so straightforward as many parameters need to be ‘tuned’ in order to provide precise control of a non-linear system.

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References

  1. A.J. Billington. Optimal PIP control of scalar and multivariable processes. In IEE Int. Conf. Pus., volume 1 of 332, pages 574–579, 1991.

    Google Scholar 

  2. I. Fletcher, I. Wilson, and C.S. Cox. CAD supported strategies for MIMO systems: Illustrative solutions using a coupled tanks experiment. Transactions of the Institute of Measurement and Control, 1995. (special edition), to appear.

    Google Scholar 

  3. T. Heckenthaler and S. Engell. Approximately time optimal fuzzy control of a two-tank system. IEEE Control Systems Journal, pages 24–30, June 1994.

    Google Scholar 

  4. P.J. King and E.H. Mamdani. The application of fuzzy control systems to industrial processes. Automatica, 13:235–242, 1977.

    Article  Google Scholar 

  5. A.G.J. MacFarlane and J.J. Belletrutti. Characteristic locus design method. Automatica, 9, 1973.

    Google Scholar 

  6. J.M. Maciejowski. Multivariable feedback design. Addison-Wesley, 1989.

    Google Scholar 

  7. H.H. Rosenbrock. Computer aided control system design. Academic Press, London, 1974.

    Google Scholar 

  8. R.M. Tong. A control engineering review of fuzzy systems. Automatica, 13:559–569, 1977.

    Article  Google Scholar 

  9. I. Wilson and I.G. French. Genetically tuned time optimal fuzzy control. In Advances in Process Control IV, I Chem E., York, September 27–28 1995.

    Google Scholar 

  10. P.C. Young, M.A. Behzadi, C.L. Wang, and W.A. Chotai. Direct digital and adaptive control by input output state variable feedback pole assignment. Int. J. of Control, 46(6), 1987.

    Google Scholar 

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© 1998 Springer-Verlag Wien

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Wilson, I., French, I.G., Fletcher, I., Cox, C.S. (1998). MIMO Fuzzy Logic Control of a Liquid Level Process. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_134

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  • DOI: https://doi.org/10.1007/978-3-7091-6492-1_134

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83087-1

  • Online ISBN: 978-3-7091-6492-1

  • eBook Packages: Springer Book Archive

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