Guaranteed cost state estimation of stochastic uncertain systems with slope bounded nonlinearities via the use of dynamic multipliers

This paper presents a new approach to robust nonlinear state estimation based on the use of integral quadratic constraints and minimax LQG control. The approach involves a class of state estimators which include copies of the slope bounded nonlinearities occurring in the plant. Integral Quadratic Constraints and dynamic multipliers are introduced to exploit these repeated nonlinearities. 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 the closed loop value of a quadratic cost functional.