Finite-Horizon Discrete-Time Robust Guaranteed Cost State Estimation for Nonlinear Stochastic Uncertain Systems

摘要

This paper presents a new approach to discrete-time robust nonlinear state estimation based on the use of Sum Quadratic Constraints. The approach involves a class of state estimators which include copies on the system nonlinearities in the state estimator. The nonlinearities being considered are those which satisfy a generalized monotonicity condition. The linear part of the state estimator is synthesized using minimax LQG control theory and this leads to a nonlinear state estimator which gives an upper bound on an estimation error cost.