Title :
Online identification of mechanical parameters using extended Kalman filters
Author :
Schütte, F. ; Beineke, S. ; Rolfsmeier, A. ; Grotstollen, H.
Author_Institution :
Inst. for Power Electron. & Electr. Drives, Paderborn Univ., Germany
Abstract :
For the high performance speed and position control of electrical drives featuring elasticity, the observation of nonmeasurable states becomes necessary if only motor speed and current can be measured. In the presence of time-varying inertia or stiffness, online identification of these parameters is also needed, so that controller adaptation becomes feasible. In this paper, extended Kalman filters are applied and optimized for electrical drives modelled as one- or two-mass systems, respectively
Keywords :
Kalman filters; control system analysis; filtering theory; machine control; machine theory; motor drives; observers; parameter estimation; position control; velocity control; control simulation; controller adaptation; electrical drives; extended Kalman filters; mechanical parameters identification; one-mass system; online identification; position control; speed control; stiffness; time-varying inertia; two-mass system; Current measurement; Elasticity; Electric variables measurement; Filters; Friction; Nonlinear dynamical systems; Position control; Position measurement; Power measurement; Velocity measurement;
Conference_Titel :
Industry Applications Conference, 1997. Thirty-Second IAS Annual Meeting, IAS '97., Conference Record of the 1997 IEEE
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-4067-1
DOI :
10.1109/IAS.1997.643069
