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Inverse dynamics control of flexible-link manipulators using neural networks

Experimental evaluation of the performance of neural network-based controllers for tip position tracking of flexible-link manipulators is presented. A modified output re-definition approach is utilized to overcome the problem caused by the non-minimum phase characteristic of the flexible-link system. This modification is based on using minimum a priori knowledge about the system dynamics. The modified output redefinition approach requires a priori knowledge about the linear model of the system and no a priori knowledge about the payload mass. Four different neural network schemes are proposed. The neural networks are trained and employed as online controllers. The four proposed neural network controllers are implemented on a single flexible-link experimental test-bed. Experimental and simulation results are presented to illustrate the advantages and improved performance of the proposed tip position tracking controllers over conventional PD-type controllers in the presence of unmodeled dynamics.