Tech report: MNNs for adaptive control

[K.P.Unnikrishnan]

The following technical report is now available. For a hard copy, please
send your surface mailing address to sastry at neuro.cs.gmr.com. ftp versions
of the paper and the actual code for simulations may be available in future.

Unnikrishnan
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Memory Neuron Networks for Identification and Control of Dynamical Systems

P. S. Sastry, G. Santharam 
Indian Institute of Science
and
K. P. Unnikrishnan 
General Motors Research Laboratories 

This paper presents  Memory Neuron Networks as models for identification and 
adaptive control of nonlinear dynamical systems. These are a class of recurrent
networks obtained by adding  trainable temporal elements to  feed-forward 
networks which makes the output history sensitive. By virtue of this capability,
these networks can identify dynamical systems without having to be explicitly 
fed with   past inputs and outputs. Thus, they can identify systems whose order
is unknown or systems with unknown delay. It is argued that for satisfactory 
modeling of dynamical systems, neural networks should be endowed with such 
internal memory. The paper presents a preliminary analysis of the learning 
algorithm,  providing  theoretical justification for the identification method.
Methods for adaptive control of nonlinear systems using these networks are 
presented. Through extensive simulations, these models are shown to be effective
both for identification and model reference adaptive control of nonlinear 
systems.