Title :
Online identification of nonlinear mechanics using extended Kalman filters with basis function networks
Author :
Beineke, S. ; Schütte, F. ; Grotstollen, H.
Author_Institution :
Inst. for Power Electron. & Electr. Drives, Paderborn Univ., Germany
Abstract :
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
Keywords :
Kalman filters; adaptive control; angular velocity control; electric drives; machine control; nonlinear systems; parameter estimation; position control; time-varying systems; adaptive controllers; basis function networks; deterministic parameter variations; electrical drives; extended Kalman filters; learning; nonlinear load characteristics; nonlinear mechanics; online identification; parameter characteristics; position control; speed control; time-varying inertia conditions; time-varying load conditions; Differential equations; Ear; Kalman filters; Measurement uncertainty; Noise measurement; Nonlinear dynamical systems; Nonlinear equations; Signal processing algorithms; State estimation; Vectors;
Conference_Titel :
Industrial Electronics, Control and Instrumentation, 1997. IECON 97. 23rd International Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-3932-0
DOI :
10.1109/IECON.1997.671069