Multi-objective T-S fuzzy control of Covid-19 spread model: An LMI approach. (January 2023) - Najarzadeh, Reza; Asemani, Mohammad Hassan; Dehghani, Maryam; Shasadeghi, Mokhtar
Highlights: A T-S fuzzy model is extracted from the nonlinear model of the covid-19 outbreak. A robust controller controller is designed for the T-S fuzzy model. Some control objective including LQR and mixed H2 − H∞ are taken into account as an optimization control problem to deal with covid-19 model challenges. Different situations of the problem are investigated based on the simulation. The results verify that we can tackle the hazards of the covid-19 outbreak effectively if the suggested approaches and policies are executed appropriately. Abstract: Due to the importance of control actions in spreading coronavirus disease, this paper is devoted to first modeling and then proposing an appropriate controller for this model. In the modeling procedure, we used a nonlinear mathematical model for the covid-19 outbreak to form a T-S fuzzy model. Then, for proposing the suitable controller, multiple optimization techniques including Linear Quadratic Regulator (LQR) and mixed H 2 - H ∞ are taken into account. The mentioned controller is chosen because the model of corona-virus spread is not only full of disturbances like a sudden increase in infected people, but also noises such as unavailability of the exact number of each compartment. The controller is simulated accordingly to validate the results of mathematical calculations, and a comparative analysis is presented to investigate the different situations of the problem. Comparing the results of controlled and uncontrolled situations, it can be observed that we can tackle the devastating hazards of the covid-19 outbreak effectively if the suggested approaches and policies of controlling interventions are executed, appropriately.