Abstract
Self-adaptive predictive control (SAP) systems adjust their behavior in response to the changing physical system in order to achieve improved control. As such, models of self-adaptive control systems result in time variance of parameters. This significantly increases the complexity of model checking verification and reachability analysis techniques. In this paper, we explore recent studies on co-simulation of SAP controllers and propose a novel co-simulation platform that can be used to analyze the effectiveness of verification and reachability analysis techniques developed for SAP controllers.
This work has been partly funded by NIH grant EB019202. Thanks to Yi Zhang from CDRH, FDA for introducing the authors to the artificial pancreas model and regulatory issues.
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Lamrani, I., Banerjee, A., Gupta, S.K.S. (2018). Co-simulation of Physical Model and Self-Adaptive Predictive Controller Using Hybrid Automata. In: Mazzara, M., Ober, I., Salaün, G. (eds) Software Technologies: Applications and Foundations. STAF 2018. Lecture Notes in Computer Science(), vol 11176. Springer, Cham. https://doi.org/10.1007/978-3-030-04771-9_7
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