Neural Networks and Identification of Systems With Unobserved States

[+] Author and Article Information
C. J. Goh, Lyle Noakes

Department of Mathematics, University of Western Australia, Nedlands, Australia 6009

J. Dyn. Sys., Meas., Control 115(1), 196-203 (Mar 01, 1993) (8 pages) doi:10.1115/1.2897398 History: Received June 01, 1991; Revised December 01, 1991; Online March 17, 2008


Consider a nonlinear control system, whose structure is not known (apart from the order of the system) and whose states are not observed. We observe the output of the system for a period of time using persistently exciting input, and use the observation to train a neural network emulator whose output approximates that of the original system. We point out that such an explicit dynamical relationship between the input and the output is useful for the purpose of construction of output feedback controller for nonlinear control systems. Specialization of the method to linear systems allows swift convergence and parameter identification in some cases.

Copyright © 1993 by The American Society of Mechanical Engineers
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