Parametric identification of robotic systems with stable time-varying Hopfield networks

Authors:
Miguel Atencia;Gonzalo Joya;Francisco Sandoval
Affiliations:
Universidad de Málaga, Departamento de Matemática Aplicada, E.T.S.I.Telecomunicación, Complejo Politécnico, Campus de Teatinos, s/n, 29071, Málaga, Spain;Universidad de Málaga, Departamento de Tecnología Electrónica, E.T.S.I.Telecomunicación, Complejo Politécnico, Campus de Teatinos, s/n, 29071, Málaga, Spa ...;Universidad de Málaga, Departamento de Tecnología Electrónica, E.T.S.I.Telecomunicación, Complejo Politécnico, Campus de Teatinos, s/n, 29071, Málaga, Spa ...
Venue:
Neural Computing and Applications
Year:
2004

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Abstract

In this work, a novel method for on-line identification of non-linear systems is proposed based upon the optimisation methodology with Hopfield neural networks. The original Hopfield model is adapted so that the weights of the resulting network are time-varying. A rigorous analytical study proves that, under mild assumptions, the estimations provided by the method converge to the actual parameter values in the case of constant parameters, or to a bounded neighbourhood of the parameters when these are time-varying. Time-varying parameters, often appearing in mechanical systems, are dealt with by the neural estimator in a more natural way than by least squares techniques. Both sudden and slow continuous variations are considered. Besides, in contrast to the gradient method, the neural estimator does not critically depend on the adjustment of the gain. The proposed method is applied to the identification of a robotic system with a flexible link. A reduced output prediction error and an accurate estimation of parameters are observed in simulation results.