A State and Parameter Identification Scheme for Linearly Parameterized Systems

[+] Author and Article Information
Chia-Shang Liu, Huei Peng

Department of Mechanical Engineering and Applied Mechanics, University of Michigan, Ann Arbor, MI 48109-2125

J. Dyn. Sys., Meas., Control 120(4), 524-528 (Dec 01, 1998) (5 pages) doi:10.1115/1.2801496 History: Received May 05, 1997; Online December 03, 2007


This paper presents an adaptive algorithm to estimate states and unknown parameters simultaneously for nonlinear time invariant systems which depend affinely on the unknown parameters. The system output signals are filtered and reparameterized into a regression form from which the least squares error scheme is applied to identify the unknown parameters. The states are then estimated by an observer based on the estimated parameters. The major difference between this algorithm and existing adaptive observer algorithms is that the proposed algorithm does not require any special canonical forms or rank conditions. However, an output measurement condition is imposed. The stability and performance limit of this scheme are analyzed. Two examples are then presented to show the effectiveness of the proposed schemes.

Copyright © 1998 by The American Society of Mechanical Engineers
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