Abstract
This paper presents a predictive control method based on switching among a set of mathematical models for a nonlinear experimental process. An adaptive predictive control configuration with multi-model identification scheme is appropriate to deal with systems subjected to sudden parameter changes or running at several operating points with different characteristics. The methodology uses a set of discrete-time mathematical models (multiple models) of the process obtained from an off-line technique by fast evolutionary programming with mutation operator based on Cauchy distribution. Experimental tests, identification and control tasks are carried out in a laboratory scale fan-and-plate plant. The good performance shown by the predictive controller based on multiple models confirms the usefulness and robustness of the proposed control method.
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© 2002 Springer-Verlag London
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Dos Santos Coelho, L., da Mota Almeida, O., Sumar, R.R., Coelho, A.A.R. (2002). Predictive Control of a Nonlinear Process Using Multiple Models Optimization Based on Fast Evolutionary Programming. In: Roy, R., Köppen, M., Ovaska, S., Furuhashi, T., Hoffmann, F. (eds) Soft Computing and Industry. Springer, London. https://doi.org/10.1007/978-1-4471-0123-9_16
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DOI: https://doi.org/10.1007/978-1-4471-0123-9_16
Publisher Name: Springer, London
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