Discrete time mpc for switched systems with applications to biomedical problems

FERRAMOSCA, ANTONIO; ANDERSON, ALEJANDRO; HERNANDEZ VARGAS, ESTEBAN ABELARDO; GONZÁLEZ, ALEJANDRO HERNÁN

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION

ELSEVIER SCIENCE BV

Lugar: Amsterdam; Año: 2020 vol. 95

1007-5704

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 au- tonomous system - at each time step - among a given finite family. Even when this se- lection 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 for- mulation 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 strat- egy outperforms the schedule for available treatments.