Optimal Robust LPV Control Design for Novel Covid-19 Disease

Abstract

These days almost all countries around the world are struggling with coronavirus outbreak. If the governments and public health care systems don't take any action against this outbreak, it would have severe effects on human life, now and in the future. By doing so, there are several intervention strategies that could be implemented and as the result, the societies become more secure from the casualties of this virus. In this paper, we used a mathematical model of coronavirus epidemic transmission and by use of some LMIs, a robust LPV controller is designed which helps us to choose and use the intervention methods, effectively. By use of the proposed robust controller, the robustness and stability of the model against a wide range of uncertainties are approved. The final objective of this control design is to minimize the number of exposed and infected individuals in the compartmental model. In the end, it can be seen that the control strategies which are preventive action, good medical care, and sterilization of the environment, can highly reduce the negative effects of the coronavirus.

Multi-objective T-S fuzzy control of Covid-19 spread model: An LMI approach

“…From now on, and for simplicity, we will mention the robust LPV and state feedback LPV approaches with LPV and SF abbreviations, respectively. It should be noted that the robust LPV approach for this part is borrowed from our previous work [25] , which is mixed with two other works [40] , [41] to improve the desired response. Fig.…”

“…The premise variables for every uncertain or varying parameter of the T-S fuzzy model in the above matrices are derived in the following form by use of the sector nonlinearity approach [25] : …”

“…The simulation results will suggest strategies such as rate of vaccination and hospitalization and a lockdown policy to control the covid-19 outbreak. The proposed method acts better than the robust Linear Parameter Varying (LPV) controller proposed in [25] since it seeks the optimum result of dealing with the covid-19 model with minimum control effort and mathematical calculation. As the natural system consists of both uncertainties and nonlinearities, working on this method helps us consider and get more realistic results from our simulation.…”