Tip-position tracking for flexible-link manipulators using artificial neural networks: experimental results

Presents experimental evaluation of the performance of neural network-based controllers for tip position tracking of flexible-link manipulators. Four different neural network schemes are proposed based on the output re-definition approach. The new output is defined assuming no a priori knowledge about the payload mass. The first two schemes are developed using a modified version of the “feedback-error-learning” approach to learn the inverse dynamics of the flexible manipulator. Both schemes require only a linear model of the system for defining the new outputs and for designing conventional PD-type controllers. This assumption is relaxed in the third and fourth schemes. The 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 robust performance of the proposed schemes in the presence of unmodeled dynamics such as hub friction, stiction and payload variations