By using the delay partitioning technology, new criteria are derived which guarantee the filtering error system to be regular, causal, stochastically stable with a certain H∞ performance index.
The mode-dependent conditions are established to guarantee the filtering error system to be robustly stochastically stable and achieve a prescribed mixed H∞ and passivity performance index.
A sufficient condition is derived to guarantee the filtering error system is exponentially mean-square stable with a prespecified H∞ performance.
Based on the fast adaptive fault estimation (FAFE) algorithm, our attention is focused on the design of fault estimation filters to guarantee the filtering error system to be asymptotically stable with a prescribed H∞ performance.
Our attention is focused on the design of full- and reduced-order filters that guarantee the filtering error system to be stochastically stable with a prescribed weighted ℓ2−ℓ∞ performance.
With the help of the Bessel Legendre stochastic inequality and the new Lyapunov krasovskii functional, an L2−L∞ filter is developed, which can guarantee the filtering error system to be asymptotically mean-square stable with a prescribed L2−L∞ performance level.
By using the delay partitioning technique, a delay-dependent condition is established to guarantee the filtering error systems to be stochastically admissible and achieve a prescribed l2 l∞ performance index.
By considering auxiliary slack variables with free structure and introducing additional scalar parameters, new sufficient conditions for H∞ filter design are presented in terms of linear matrix inequalities (LMIs), which guarantee the filtering error systems to be asymptotically stable with prescribed H∞ performances.
An analytical solution, which guarantees the filter gain matrix to be an optimal one, is then obtained.
Moreover, a desired filter is constructed based on the performance analysis, which guarantees the filtering error system to be stochastically stable and satisfying a prescribed H∞ performance level.
