No abstract

No abstract

Nowadays, interconnected cyber-physical systems (CPSs) are widely used with increasing deployments of Industrial Internet of Things (IIoT) applications. Other than operating properly under system uncertainties, CPSs should be secured under unwanted adversaries. To mark such challenges, this paper proposes the solution of secure decentralized robust control for uncertain CPSs under replayed time-delay and false-data injection attacks altogether. Potentially, considered attacks can force the whole system to instability and crash. Three challenges are addressed, and solutions are presented: (1) model non-linearity and uncertainties, (2) existing simultaneous time-delay and potential false-data injection attacks with skew probability density functions, and (3) requirement to use real-time attack detection. Thus, a novel, robust control method to deal with thwart attacks on a closed-loop control system is proposed to provide the system's trustworthiness. Additionally, novel attack detection methodologies are presented to detect these advanced attacks rapidly based on statistical methods such as Spearman's correlation coefficient, Neyman-Pearson (NP) error classification, and trend analysis. Ultimately, the proposed novel attack detection and robust control protocol are verified and evaluated in real-time.

Due to the prevalence of Human Immuno-deficiency Virus/Acquired Immuno-Deficiency Syndrome (HIV/AIDS) infection in society and the importance of preventing the spread of this disease, a mathematical model for sexual transmission of HIV/AIDS epidemic with asymptomatic and symptomatic phase and public health education is stated as a symmetric system of differential equations in order to reduce the spread of this infectious disease. It is demonstrated that public health education has a considerable effect on the prevalence of the disease. Moreover, the cost of education is very high and for this reason, a cost-optimal control is applied to provide the best possible combination of the parameters corresponding to education in controlling the spread of the disease by means of the Genetic Algorithm (GA) and Simulated Annealing (SA).

In this paper, the covariance control algorithm for nonlinear stochastic systems using covariance feedback is studied. Covariance control of nonlinear systems scenario involves the theory of covariance control based on the idea of the covariance feedback. Therefore, the proposed covariance control algorithm is derived for our case, firstly by applying the covariance control method and linear approximation of nonlinear systems, and then it is achieved by adopting this method for a class of nonlinear stochastic systems by using feedback linearization idea and a covariance feedback controller. The effectiveness of the proposed covariance feedback algorithm is studied using numerous simulations concerning different nonlinear case studies.