RESUMEN
This paper considers the problem of non-fragile state estimation under dissipative constraint for a class of nonlinear cyber-physical systems (CPSs) with sensor delays. The dynamics of the considered CPSs is characterized by the well-known T-S fuzzy model and system measurements are valued by wireless sensors. The communication link between the filter and the plant is described by a relatively practical model and sensor delays occurred in signal transmissions are taken into consideration. A stochastic variable which yields the standard Bernoulli distribution is exploited to model sensor delays encountered by the sensor measurements. With the help of a basis-dependent Lyapunov function and predefined performance constraint, sufficient conditions are then developed to establish the stochastic stability as well as strict dissipativity for the resultant filtering error system. The existence of the corresponding filter is guaranteed and the expression of desired filter parameters are shown explicitly. In the end, the established theoretical results are validated by a tunnel diode circuit example and corresponding simulations are also provided.
RESUMEN
This paper is concerned with the problem of secure state reconstruction for cyber-physical systems (CPSs). CPSs are more vulnerable to the cyber world yet to attackers, who can attack any sensor of the considered systems and modify values of attacked sensors to be arbitrary ones. In the design process, both malicious attacks on sensors and unknown input are taken into consideration. First, a linear discrete-time state-space model is utilized to describe such systems, and then a sparse vector is adopted to model attacks. By collecting sensor measurements and using an iterative approach, a new model in descriptor form is obtained, which paves the way for estimating system states under an unknown input situation. Second, the problem of secure state estimation is transformed into an optimal version. A novel sliding-mode observer is proposed to estimate system states from collected sensor measurements corrupted by malicious attacks. In order to guarantee the estimations to be sparse, a projection operator is designed. Third, a projected sliding-mode observer-based estimation algorithm is developed to reconstruct system states, where an event-triggered scheme is integrated to save limited computational resource. In addition to propose such an algorithm, the effectiveness of both projection operator and sliding-mode observer is analyzed. Furthermore, the convergence of the proposed secure estimation algorithm is proved. Finally, some simulation results are given to demonstrate the effectiveness of the proposed algorithm.