A prediction-error-method for recursive identification of nonlinear systems
Nonlinear systems can be identified with nonlinear filters by combined estimation of parameters and state. Often these methods are very complex or have convergence problems, as for example the Extended Kalman Filter. In this paper a new algorithm for recursive nonlinear system identification is presented. Convergence problems are eliminated by an improved calculation of the gradient as the total derivative of the prediction error. By separately estimating parameters and states, computation time is reduced. The efficiency of the new identification algorithm is illustrated by studying its performance in a gravimetric filling system.
combined estimation of parameters and state
recursive nonlinear system identification
scientific article; zbMATH DE number 4002007