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Reduced Kalman filtering for indirect adaptive control of the induction motor
Author(s) -
Glielmo L.,
Marino P.,
Setola R.,
Vasca F.
Publication year - 1994
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.4480080602
Subject(s) - control theory (sociology) , extended kalman filter , kalman filter , observer (physics) , induction motor , alpha beta filter , computer science , control engineering , noise (video) , controller (irrigation) , state observer , vector control , engineering , control (management) , moving horizon estimation , artificial intelligence , nonlinear system , physics , agronomy , quantum mechanics , voltage , electrical engineering , image (mathematics) , biology
Abstract Parameter variations strongly affect the application of indirect field‐oriented control (FOC) of the induction motor. To estimate those parameters which cannot be obtained by means of a direct measure, an augmented state observer can be constructed; in particular, the presence of noise introduced by the inverter and sensors suggests the use of an extended Kalman filter (EKF). In this paper, by analysing the indirect FOC technique via a two‐time‐scale approach, we formally justify its effectiveness and propose a reduced‐order EKF which is used to construct an adaptive version of the FOC method. The practical drawback of reduced‐order EKFs, i.e. the necessity to differentiate measured quantities, is avoided by exploiting some peculiarities of the field‐oriented controller.