In recent years, much attention has been focused upon predictive control of nonlinear systems. The implementation of such a control strategy for real processes has greatly improved their performance. This paper deals with a model-based predictivecontrol (MBPC) strategy using a generalised Hammerstein model and its application to the temperature control of a semibatch reactor. Both unconstrained and constrained adaptive control problems are considered. A simple identification method based on the weighted recursive least squares method (WRLS) is used to estimate the model parameters on-line. An indirect adaptive nonlinear controller is designed by combining the predictive controller with an indirect parameter estimation algorithm. This adaptive scheme has been applied for the control of a semi-batch chemical reactor. Experimental results show that the performance of the generalised Hammerstein MBPC (NLMBPC) was significantly better than that of a linear model predictive controller (LMBPC).
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ID="A1"Correspondance and offprint requests to: Dr F. M'sahli, Department of Electrical Engineering, ISETKH, Avenue Hadj Ali Soua, 5070, Ksar Hellal, Monastir, Tunisia. E-mail: msahli-fn@iyahoo.fr
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Abdennour, R., Ksouri, M. & M'Sahli, F. Nonlinear Model-Based Predictive Control Using a Generalised Hammerstein Model and its Application to a Semi-Batch Reactor. Int J Adv Manuf Technol 20, 844–852 (2002). https://doi.org/10.1007/s001700200225
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DOI: https://doi.org/10.1007/s001700200225