Archivo Digital UPM: No conditions. Results ordered -Date Deposited. 2019-12-16T09:18:51ZEPrintshttp://oa.upm.es/style/images/logo-archivo-digital.pnghttp://oa.upm.es/2019-04-09T18:04:43Z2019-04-09T18:04:43Zhttp://oa.upm.es/id/eprint/54595This item is in the repository with the URL: http://oa.upm.es/id/eprint/545952019-04-09T18:04:43ZLuenberger observer with nonlinear structure applied to diabetes type 1In this work a Luenberger observer (LO) for type 1 diabetes is established using the Hovorka?s model (HM). The HM is linearized around an operating point and the eigenvalues are calculated. The LO is designed relocating the HM eigenvalues through the Ackermann?s methodology for linear observers where the proposed LO keeps the nonlinear structure of the model system. The LO is parameterized and tuned with the mean from six virtual patients of HM. Once the observer performance is reliable estimating the state space variables for HM, the virtual patients are changed by patients of Bergman?s model in order to test the observer behavior under unknown dynamics. These estimated variables constitute the ones corresponding to HM. The variables are estimated by the data computational processing which correspond to the insulin (input) and glucose (output) of the virtual patients. The estimated variables by the LO are very similar for virtual patients generated by both models, where the parameter FIT is used to quantify the performance of the observer. The computational implementation of the LO is useful tool to estimate the unmeasured variables in diabetic patients so they can be used in the artificial pancreas.Onofre Orozco LópezCarlos Eduardo Castañeda HernándezAgustin Rodriguez HerreroGema García SaezMaria Elena Hernando Perez2019-04-09T17:55:32Z2019-04-09T17:55:32Zhttp://oa.upm.es/id/eprint/54571This item is in the repository with the URL: http://oa.upm.es/id/eprint/545712019-04-09T17:55:32ZLinear time-varying Luenberger observer applied to diabetesWe present a linear time-varying Luenberger observer (LTVLO) using compartmental models to estimate the unmeasurable states in patients with type 1 diabetes. The LTVLO proposed is based on the linearization in an operation point of the virtual patient (VP), where a linear time-varying system is obtained. LTVLO gains are obtained by selection of the asymptotic eigenvalues where the observability matrix is assured. The estimation of the unmeasurable variables is done using Ackermann’s methodology. The Lyapunov approach is used to prove the stability of the time-varying proposal. In order to evaluate the proposed methodology, we designed three experiments: A) VP obtained with Bergman’s minimal model, B) VP obtained with Hovorka’s model, and C) real patient data set. For both experiments A) and B), it is applied a meal plan to the VP, where the dynamic response of each state model is compared to the response of each variable of the time-varying observer. Once the observer is obtained in experiment B), the proposal is applied to experiment C) with data extracted from real patients and the unmeasurable state space variables are obtained with the LTVLO. LTVLO methodology has the feature of being updated each time instant to estimate the states under a known structure. The results are obtained using simulation with M atlabTM and SimulinkTM. The LTVLO estimates the unmeasurable states from in silico patients with high accuracy by means of the update of Luenberger gains at each iteration. The accuracy of the estimated state space variables is validated through the fit parameter.Onofre Orozco LópezCarlos Eduardo Castañeda HernándezAgustin Rodriguez HerreroGema García SaezMaria Elena Hernando Pérez