Nonlinear model predictive control with logic constraints for COVID-19 management

Abstract

The management of COVID-19 appears to be a long-term challenge, even in countries that have managed to suppress the epidemic after their initial outbreak. In this paper, we propose a model predictive approach for the constrained control of a nonlinear compartmental model that captures the key dynamical properties of COVID-19. The control design uses the discrete-time version of the epidemic model, and it is able to handle complex, possibly time-dependent constraints, logical relations between model variables and multiple predefined discrete levels of interventions. A state observer is also constructed for the computation of non-measured variables from the number of hospitalized patients. Five control scenarios with different cost functions and constraints are studied through numerical simulations, including an output feedback configuration with uncertain parameters. It is visible from the results that, depending on the cost function associated with different policy aims, the obtained controls correspond to mitigation and suppression strategies, and the constructed control inputs are similar to real-life government responses. The results also clearly show the key importance of early intervention, the continuous tracking of the susceptible population and that of future work in determining the true costs of restrictive control measures and their quantitative effects.

Event-triggered model predictive control of positive systems with random actuator saturation

“…Meanwhile, all existing results on the MPC of positive systems are concerned with linear systems. There are no results on the MPC of nonlinear positive systems though some attention has been paid to the same topic of nonlinear systems (non-positive) [ 38 , 51 , 52 ]. The nonlinearity of positive systems is essentially difficult to be explored since there are no tractable methods to define the positivity of a nonlinear system [ 46 ].…”

Controlling Epidemic Diseases Based only on Social Distancing Level

“…2020 ; Péni et al. 2020 ; Morato et al. 2020a , b ) propose new models and new nonlinear estimators to do this.…”

“…To capture the key dynamical properties of COVID-19, Péni et al. ( 2020 ) proposes a model predictive approach for constrained control. Morato et al.…”

“…Most advanced feedback control methods need all state information for computing the input, which is not realistic to assume (Péni et al. 2020 ). This assumption implies that the number of individuals in each compartment is continuously measured.…”

“…Sélley et al (2015) applied a nonlinear model predictive control to model a susceptible-infectioussusceptible epidemic on nontrivial contact structures. To capture the key dynamical properties of COVID-19, Péni et al (2020) proposes a model predictive approach for constrained control. Morato et al (2020a) formulates a Model Predictive Control policy to mitigate the COVID-19 contagion in Brazil.…”