BLACK BOX CLOSED LOOP ROBOT MANIPULATOR SYSTEM IDENTIFICATION

Document Type : Original Article

Authors

1 Aziz I. Said is a Prof. Dr. with the Department of Electrical Power and Machine, Faculty of Engineering, Ain Shams University. Egypt, Cairo.

2 Ashraf S. Awad is with Quality Control of Machining production Department, in Egyptian Tank Plant, MF 200, Abo Zabal, Cairo, Egypt.

Abstract

The paper discusses experimental identification of one joint of a hand made, two degrees of freedom robot manipulator, including flexibilities, under feedback. A black box system model is identified from the input-output data. Both linear, OE (Output Error) and non-linear structure (multilayer perceptrons neural network) models are treated and applied. A Levenberg-Marquardt algorithm is implemented to generate our NNARX model. As regressors two past inputs and two past outputs are chosen. Furthermore network architecture is chosen with 5 hidden tanh units and one linear output unit. Fit criteria shows that the linear model has severe problems. Validation of the trained non-linear network looks quite satisfactory, and it is definitely better than the linear model. Experience has shown that regularization is helpful when pruning neural networks. A remarkable improvement in performance, when using long instead of short format for choosing neural network weights and Bias, is appreciated.

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