Finite-horizon discrete-time robust guaranteed cost state estimation for nonlinear stochastic uncertain systems

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.