Author: Chan, V. M.; Hernandez-Vargas, E. A.; Sanchez, E. N.
Title: Neural inverse optimal control applied to design therapeutic options for patients with COVID-19 Cord-id: q3h7gq4f Document date: 2021_1_1
ID: q3h7gq4f
Snippet: In this paper we apply an inverse optimal controller (IOC) based on a control Lyapunov function (CLF) to schedule theoretical therapies for the novel coronavirus disease (COVID-19). This controller can represent the viral dynamics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in the host. The virus dynamics consider the antiviral effects and immune responses as control inputs. The proposed controller is based on a Recurrent High Order Neural Network (RHONN) used as an identifie
Document: In this paper we apply an inverse optimal controller (IOC) based on a control Lyapunov function (CLF) to schedule theoretical therapies for the novel coronavirus disease (COVID-19). This controller can represent the viral dynamics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in the host. The virus dynamics consider the antiviral effects and immune responses as control inputs. The proposed controller is based on a Recurrent High Order Neural Network (RHONN) used as an identifier trained with Extended Kalman Filter (EKF). Simulations show that applying treatment 2 days post symptoms would not significantly alter the viral load. The proposed controller to stimulate the immune response displays a better effectiveness compared to the effectiveness displayed by the antiviral effects. © 2021 IEEE.
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