Indirect adaptive structure for multivariable neural identification and control of a pilot distillation plant |
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| Authors: | J. Fernandez de Canete P. Del Saz-Orozco I. Garcia-Moral S. Gonzalez-Perez |
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| Affiliation: | 1. Mechanical Engineering Department, University of Maryland, College Park, MD 20742, United States;2. School of Mechanical Engineering, Pusan National University, Busan, South Korea;1. Dipartimento di Informatica ed Applicazioni “R.M. Capocelli”, Università di Salerno, via Ponte Don Melillo, I-84084 Fisciano (SA), Italy;2. School of Computing, Teesside University, Borough Road, Middlesbrough, TS1 3BA, UK;1. University of Concepción, Faculty of Physical and Mathematical Sciences, PO Box 160-C, Concepción, Chile;2. University Andrés Bello, Faculty of Exact Sciences, Department of Physics, República 220, Santiago, Chile;3. University of La Serena, Faculty of Engineering, Department of Mechanical Engineering, Casilla 554, La Serena, Chile;4. Center for Technological Information, c/Monseñor Subercaseaux 667, La Serena, Chile |
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| Abstract: | This paper describes the design and implementation of an indirect adaptive controller that uses neural networks both for identification and control of an experimental pilot distillation column containing a mixture of ethanol and water. The MATLAB platform is applied both for the neural identification and control of the distillation plant using the Levenberg–Marquardt approach, enabling also optimal input/output net configuration. The neural controller performance has been analyzed and illustrated via experimental tests on the pilot distillation column monitored under the LabVIEW platform. Both platforms have been linked together by constituting an integrated process control interface. The obtained experimental results demonstrate the effectiveness of the neural indirect adaptive control scheme as compared to proportional–integrative–derivative, when real-time multivariable control is demanded, even in presence of disturbances. |
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