We review some existing results on H2 and H1 estimation and explore possible connections between the optimal algorithms. For instance, in order to relate the H2 optimal Kalman lter to the H1 lters we show that, with special choices of the covariance matrices, the Kalman lter is H1 optimal. Moreover, by studying the matrix operator relating the estimation errors and the disturbances, we obtain s...

Due to its distributed nature, a cyber-physical system is vulnerable to various faults, including sensory integrity attacks. Such faults need to be accounted for in the design of a state estimator. In this paper, we consider sparse sensor faults, in which a small unknown group of sensors can be compromised. We first show a necessary condition that allows the state to be estimated in the presenc...

In this paper we propose some new solutions to the full information mixed H 2 =H 1 state feedback problem: the H 2 norm of the closed-loop system is minimized over a class of linearly combined state feedback laws that satisfy the speciied H 1 bound. Such mixed linearly combined controllers yield smaller closed-loop H 2 norms than those obtained by using the central solutions of the H 1 problem....

A worst-case mixed H 2 =H 1-optimal control problem is formulated and solved for sampled-data systems. The solution is obtained in terms of a set of synthesis equations, which correspond to similar synthesis equations for the continuous and discrete mixed H 2 =H 1 problems. The mixed H 2 =H 1 problem can be applied to the design of controllers which achieve robust H 2 performance for uncertain ...