Online identification of nonlinear mechanics using extended Kalman filters with basis function networks

For high performance speed and position control of electrical drives, fast online identification is needed for time-varying inertia or load conditions in combination with adaptive controllers. In this paper extended Kalman filters are applied and optimized for deterministic parameter variations by integrating basis function networks into the common structure of the Kalman filter. It is shown that learning of nonlinear load or parameter characteristics becomes feasible by this measure and the performance of the extended Kalman filter can be improved.