Design a robust sliding mode controller based on the state and parameter estimation for the nonlinear epidemiological...

Area of Science:

• Engineering
• Epidemiology
• Control Theory

Background:

• Covid-19 mitigation presents a significant challenge, requiring robust engineering solutions.
• Epidemiological models are susceptible to uncertainty and noise, necessitating advanced estimation techniques.
• Previous models may not fully integrate control strategies with accurate parameter estimation.

Purpose of the Study:

• To develop an engineering-based framework for Covid-19 mitigation.
• To design and implement a robust control system for managing coronavirus spread.
• To validate the model's effectiveness using real-world data from Iran and Russia.

Main Methods:

• Utilized ordinary differential equations to model coronavirus behavior.
• Employed Long Short-Term Memory (LSTM) algorithm for parameter estimation, addressing long-term dependency issues.
• Developed an Extended Kalman Filter (EKF) to estimate state variables amidst inherent noise.
• Designed a robust sliding mode controller, with stability guaranteed by Lyapunov theorems.

Main Results:

• Simulation results demonstrate the potential of global vaccination to achieve long-term herd immunity.
• The proposed controller effectively reduces daily Covid-19 infections to below 500.
• The controller also significantly decreases daily Covid-19 deaths to under 10.

Conclusions:

• An integrated engineering approach combining advanced algorithms and control theory is effective for Covid-19 mitigation.
• Vaccination is a critical factor in achieving sustainable herd immunity.
• The developed control strategy offers a promising pathway to manage and reduce the impact of infectious disease outbreaks.