A recurrent neural-network-based real-time learning controlstrategy applying to nonlinear systems with unknown dynamics
Chow, T.W.S.
Yong Fang
Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Feb 1998
Volume: 45,
Issue: 1
On page(s): 151-161
ISSN: 0278-0046
References Cited: 18
CODEN: ITIED6
INSPEC Accession Number: 5830875
Digital Object Identifier: 10.1109/41.661316
Current Version Published: 2002-08-06
Abstract
In this paper, the authors present a real-time learning control
scheme for unknown nonlinear dynamical systems using recurrent neural
networks (RNNs). Two RNNs, based on the same network architecture, are
utilized in the learning control system. One is used to approximate the
nonlinear system, and the other is used to mimic the desired system
response output. The learning rule is achieved by combining the two RNNs
to form the neural network control system. A generalized real-time
iterative learning algorithm is developed and used to train the RNNs.
The algorithm is derived by means of two-dimensional (2-D) system theory
that is different from the conventional algorithms that employ the
steepest optimization to minimize a cost function. This paper shows that
an RNN using the real-time iterative learning algorithm can approximate
any trajectory tracking to a very high degree of accuracy. The proposed
learning control scheme is applied to numerical problems, and simulation
results are included. The results are very promising, and this paper
suggests that the 2-D system theory-based RNN learning algorithm
provides a new dimension in real-time neural control systems
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