Dynamic Recurrent Neural Networks for Control of Unknown Nonlinear Systems

[+] Author and Article Information
Liang Jin, Peter N. Nikiforuk, Madan M. Gupta

Intelligent Systems Research Laboratory, College of Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada S7N 0W0

J. Dyn. Sys., Meas., Control 116(4), 567-576 (Dec 01, 1994) (10 pages) doi:10.1115/1.2899254 History: Received February 01, 1993; Revised August 01, 1993; Online March 17, 2008


A scheme of dynamic recurrent neural networks (DRNNs) is discussed in this paper, which provides the potential for the learning and control of a general class of unknown discrete-time nonlinear systems which are treated as “black boxes” with multi-inputs and multi-outputs (MIMO). A model of the DRNNs is described by a set of nonlinear difference equations, and a suitable analysis for the input-output dynamics of the model is performed to obtain the inverse dynamics. The ability of a DRNN structure to model arbitrary dynamic nonlinear systems is incorporated to approximate the unknown nonlinear input-output relationship using a dynamic back propagation (DBP) learning algorithm. An equivalent control concept is introduced to develop a model based learning control architecture with simultaneous on-line identification and control for unknown nonlinear plants. The potentials of the proposed methods are demonstrated by simulation results.

Copyright © 1994 by The American Society of Mechanical Engineers
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