ABSTRACT
This chapter discusses produce accurate state trajectory, even in the presence of a deficient/inaccurate model and additionally identify the unknown model as well as its parameters. The approach would be very useful in modelling of the large flexible structures, robotics and many aerospace dynamic systems, which usually exhibit nonlinear behaviour. System identification work generally restricted to linear and linearized models can lead to modal analysis of the nonlinear systems. The criteria used for estimation are based on least squares and one can after some transformations obtain the recursive estimator like Kalman filter (KF). In KF literature, several alternative approaches are available to handle nonlinear state estimation problems: extended KF, second-order KF, linearized KF, and statistically linearized filter. The design of filters with accurate and predictable performance led to the filters which are termed as robust, and H-infinity norm–based H-infinity filters belong to this class of robust estimators.
