ABSTRACT
This chapter discusses the design of a filter that has certain performance robustness against possible system parameter uncertainties. It focuses on the so-called robust Kaiman filter which is concerned with the design of a fixed filter for a family of plants under consideration such that the filtering error covariance is of minimal upper bound. Similar to the Kaiman filtering for systems without uncertainty, it can be shown that the optimal estimate of a linear combination of the state is the same as the linear combination of the optimal estimate of the state. The chapter also discusses the design of robust stationary a priori filter. It also focuses on the feasibility and convergence properties of the solutions of the differential riccati equations. The chapter addresses the robust Kaiman filtering of systems with norm-bounded uncertainty through the linear matrix inequality approach.
