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Efficient Model Predictive Control Algorithm with Fuzzy Approximations of Nonlinear Models

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Adaptive and Natural Computing Algorithms (ICANNGA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5495))

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Abstract

Model predictive control (MPC) algorithms are widely used in practical applications. They are usually formulated as optimization problems. If the model used for prediction is linear (or linearized on–line) then the optimization problem is standard, quadratic one. Otherwise, it is a nonlinear, in general, non–convex optimization problem. In the latter case the numerical problems may occur and time needed to calculate the control signals cannot be determined. Therefore approaches based on linear or linearized models are preferred in practical applications. In the paper a new algorithm is proposed, with prediction which employs heuristic fuzzy modeling. The algorithm is formulated as quadratic optimization problem but offers performance very close to that of MPC algorithm with nonlinear optimization. The efficiency of the proposed algorithm is demonstrated in the control system of the nonlinear control plant with inverse response – a chemical CSTR reactor.

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Marusak, P.M. (2009). Efficient Model Predictive Control Algorithm with Fuzzy Approximations of Nonlinear Models. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2009. Lecture Notes in Computer Science, vol 5495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04921-7_46

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  • DOI: https://doi.org/10.1007/978-3-642-04921-7_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04920-0

  • Online ISBN: 978-3-642-04921-7

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