Designing a sliding mode controller for a class of multi-controller COVID-19 disease model

The recent outbreak of the COVID-19 disease has just appeared at the end of 2019 that has now become a global pandemic. Analysis of mathematical models in the prediction and control of this pandemic helps to make the right decisions about vaccination, quarantine, and other control measures. In this article, the aim is to analyze the three control measures of edu-cational campaigns, social distancing, and treatment control, that these control measures can reduce the spread of this disease. For this purpose, due to the uncertainty in the model parameters, a sliding mode control law is used. Furthermore, because the model parameters are changing and the upper limit of the parameters that have uncertainty should be known, then an adaptive control is used to estimate the switching gain online. In addition, in order to prevent the chattering phenomenon, the sign func-tion is used in the sliding control law. Also, the obtained properties are expressed and proven analytically. Therefore, initially, the controller is designed assuming certain knowledge of an upper bound of the uncertainty signal. After that, the parameters that have uncertainty in the simulation are obtained by online estimation of the adaptive control. The efficiency and performance of the controller in the absence of the certainty of the model parameters are investigated, and the results show the desired per-formance of this controller. Finally, the performance and efficiency of the controller are evaluated by simulation.