Using a new model of recurrent neural network for control

This paper shows the results obtained in controlling a mobile robot by means of local recurrent neural networks based on a radial basis function (RBF) type architecture. The model used has a Finite Impulse Response (FIR) filter feeding back each neuron's output to its own input, while using ano...

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Bibliographic Details
Published in:Neural processing letters 2001-04, Vol.13 (2), p.101-113
Main Authors: BOQUETE, L, BERGASA, L. M, BAREA, R, GARCIA, R, MAZO, M
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
FIR filters
Intelligent control
Lyapunov methods
Mobile robots
Radial basis function
Radial basis function networks
Recurrent neural networks
Wheelchairs
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Summary:This paper shows the results obtained in controlling a mobile robot by means of local recurrent neural networks based on a radial basis function (RBF) type architecture. The model used has a Finite Impulse Response (FIR) filter feeding back each neuron's output to its own input, while using another FIR filter as a synaptic connection. The network parameters (coefficients of both filters) are adjusted by means of the gradient descent technique, thus obtaining the stability conditions of the process. As a practical application the system has been successfully used for controlling a wheelchair, using an architecture made up by a neurocontroller and a neuroidentifier. The role of the latter, connected up in parallel with the wheelchair, is to propagate the control error to the neurocontroller, thus cutting down the control error in each working cycle.
ISSN:1370-4621
1573-773X
DOI:10.1023/A:1011375420498