Bounding methods for state and parameter estimation

摘要

Although bounding methods for estimation and control date back to many decades, research has intensified since the early nineties. The methods developed satisfy practical needs of engineering for various reasons: (1) It is often incorrect to assume statistics on measurement errors as that can result in estimated statistical distributions of single point estimates that are misleading. (2) Input-output signal uncertainty is mixed with model parameter uncertainties. (3) A model with certain given accuracy bounds is often to be fitted to measurement data. (4) Sensitivity analysis and estimability of model parameters are needed to validate engineering models.