A stable recursive state estimation filter for models with nonlinear dynamics subject to bounded disturbances

This contribution proposes a recursive and easily implementable online algorithm for state estimation of multi-output discrete-time systems with nonlinear dynamics and linear measurements in presence of unknown but bounded disturbances corrupting both the state and measurement equations. The proposed algorithm is based on state bounding techniques and is decomposed into two steps: time update and observation update that uses a switching estimation Kalman-like gain matrix. Particular emphasis is given to the design of a weighting factor that ensures consistency of the estimated state vectors with the input-output data and the noise constraints and that guarantees the stability of the algorithm