101. Nonlinear observer output-feedback MPC treatment scheduling for HIV.
- Author
-
Zurakowski, Ryan
- Subjects
- *
IMMUNE response , *MATHEMATICAL models , *HIV , *HIGHLY active antiretroviral therapy , *LYMPHOCYTES - Abstract
Background: Mathematical models of the immune response to the Human Immunodeficiency Virus demonstrate the potential for dynamic schedules of Highly Active Anti-Retroviral Therapy to enhance Cytotoxic Lymphocyte-mediated control of HIV infection. Methods: In previous work we have developed a model predictive control (MPC) based method for determining optimal treatment interruption schedules for this purpose. In this paper, we introduce a nonlinear observer for the HIV-immune response system and an integrated output-feedback MPC approach for implementing the treatment interruption scheduling algorithm using the easily available viral load measurements. We use Monte-Carlo approaches to test robustness of the algorithm. Results: The nonlinear observer shows robust state tracking while preserving state positivity both for continuous and discrete measurements. The integrated outputfeedback MPC algorithm stabilizes the desired steady-state. Monte-Carlo testing shows significant robustness to modeling error, with 90% success rates in stabilizing the desired steady-state with 15% variance from nominal on all model parameters. Conclusions: The possibility of enhancing immune responsiveness to HIV through dynamic scheduling of treatment is exciting. Output-feedback Model Predictive Control is uniquely well-suited to solutions of these types of problems. The unique constraints of state positivity and very slow sampling are addressable by using a special-purpose nonlinear state estimator, as described in this paper. This shows the possibility of using output-feedback MPC-based algorithms for this purpose. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF