BLACK BOX CLOSED LOOP ROBOT MANIPULATOR SYSTEM IDENTIFICATION | ||||
| International Conference on Aerospace Sciences and Aviation Technology | ||||
| Article 101, Volume 12, ASAT Conference, 29-31 May 2007, May 2007, Page 1-12 PDF (205.26 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/asat.2007.24112 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Aziz I. Said1; Ashraf S. Awad2 | ||||
| 1Aziz I. Said is a Prof. Dr. with the Department of Electrical Power and Machine, Faculty of Engineering, Ain Shams University. Egypt, Cairo. | ||||
| 2Ashraf 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. | ||||
| Keywords | ||||
| Arms control; ART neural networks; Autoregressive moving average processes; Closed loop systems; Control systems; correlation; Feedback systems; identification; Manipulators; Motor drives; Optical position measurement | ||||
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