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System Identification based on Model Synchronization
Author(s) -
Koester Marius,
Jehle Georg,
Fidlin Alexander
Publication year - 2014
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201410445
Subject(s) - synchronizing , extended kalman filter , control theory (sociology) , kalman filter , system identification , nonlinear system , computer science , transient (computer programming) , identification (biology) , clutch , synchronization (alternating current) , state (computer science) , control engineering , simple (philosophy) , engineering , algorithm , data modeling , physics , artificial intelligence , transmission (telecommunications) , control (management) , channel (broadcasting) , computer network , biology , operating system , telecommunications , quantum mechanics , mechanical engineering , botany , philosophy , database , epistemology
Detailed knowledge about system parameters is required in many technical applications in order to model the system adequately. The question how to obtain parameter values and state maps of certain components thus is of significant importance in engineering applications. In this contribution, a novel approach to identifying system parameters and parameter‐free state maps of a simple nonlinear clutch actuation device is presented. To identify parameters, the Extended Kalman Filter (EKF) is used for synchronizing a system model with measurements of the system response to transient excitation. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)