An innovation representation for nonlinear systems with application to parameter and state estimation

Abstract

An innovation model is derived for a nonlinear stochastic system described by a state variable representation. The problem of state and system parameter estimation is solved through identification of the innovation model. A recursive prediction error (RPE) algorithm is derived for the joint system parameter and state estimation through minimization of the innovation variance (MIV). The algorithm is robust against the use of an erroneous model. Convergence and stability properties of the algorithm are also analyzed. In order to ensure stability, the algorithm needs an on-line stability check at each iteration.