Discrete-time MPC for switched systems with applications to biomedical problems

摘要

This paper studies switched systems in which the manipulated control action is the time depending switching signal. To control the switched systems means to select an autonomous system - at each time step among a given finite family. Even when this selection can be done by solving a Dynamic Programming (DP) problem, such a solution is often difficult to apply, and state/control constraints cannot be explicitly considered. In this work a new set-based Model Predictive Control (MPC) strategy is proposed to handle switched systems in a tractable form. The optimization problem at the core of the MPC formulation consists in an easy-to-solve mixed-integer optimization problem, whose solution is applied in a receding horizon way. Applications to schedule therapies in viral infection and cancer treatments are studied. The numerical results suggest that the proposed strategy outperforms the schedule for available treatments. (C) 2020 Elsevier B.V. All rights reserved.