Nonlinear Model Based Predictive Control Using Multiple Models Approach Expanded on Laguerre Bases

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Abstract: This paper proposes a nonlinear model based predictive control (NMPC) algorithm for nonlinear systems by using multiple models approach. To have a less complexity model we expand each linear sub-model on an orthogonal Laguerre basis, the characteristic pole of which should be optimized. In this paper we propose a pole optimization algorithm based on the Gauss-Newton method and we use the provided Laguerre multiple model (LMM) to synthesize a NMPC algorithm. The proposed pole optimization technique as well as the NMPC using LMM approach are validated on a chemical reactor.

WSEAS Transactions on Systems and Control, ISSN / E-ISSN: 1991-8763 / 2224-2856, Volume 10, 2015, Art. #13

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Ghassen Marouani, Abdelkader Mbarek, Tarek Garna, Hassani Messaoud, "Nonlinear Model Based Predictive Control Using Multiple Models Approach Expanded on Laguerre Bases," WSEAS Transactions on Systems and Control, vol. 10, pp. 113-126, 2015, DOI:
Ghassen Marouani, Abdelkader Mbarek, Tarek Garna, Hassani Messaoud. Nonlinear Model Based Predictive Control Using Multiple Models Approach Expanded on Laguerre Bases. WSEAS Transactions on Systems and Control. 2015;10:113-126.
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