Abstract
The paper presents computationally efficient predictive control approaches to stabilization of the blood glucose concentration in patients suffering from diabetes type 1. Presented algorithms use neural and fuzzy models and quadratic programming. These algorithms offer much better control performance than the algorithms based on linear models. Moreover, their closed–loop accuracy is similar to that obtained in predictive control algorithms with full nonlinear optimisation repeated on-line. Though simple, such algorithms offer advantages resulting from its prediction capabilities.
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Ławryńczuk, M., Marusak, P.M., Tatjewski, P. (2010). Efficient Predictive Control Algorithms Based on Soft Computing Approaches: Application to Glucose Concentration Stabilization. In: Iskander, M., Kapila, V., Karim, M. (eds) Technological Developments in Education and Automation. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3656-8_77
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DOI: https://doi.org/10.1007/978-90-481-3656-8_77
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