Control of flexible-link manipulators using neural networks (Q698681)

This monograph shows experimental evaluation of the performance of neural network-based controllers for tip position tracking of flexible-link manipulators. The funding for much of the research described in this monograph was provided by the Natural Sciences an Engineering Research Council (NSERC) of Canada and by Fonds pour la Formation de Chercheurs et l'Aide à la Recherche (FCAR) of the Province of Quebec. Four different neural network schemes are proposed and implemented on the experimental test-bed. The first two control schemes proposed in this book assume a priori knowledge about the linear model of the manipulator. Experimental verification of the linear model is performed by using PD hub position control with high gain to overcome the effect of the friction. The non-minimum phase property of the flexible-link manipulator is discussed. In the third scheme, the controller is designed based on tracking the hub position while controlling the elastic deflection at the tip. In the fourth scheme which employs two neural networks, the first network is developed for specifying an appropriate output for ensuring minimum phase behavior of the system. In the second neural network is implemented an inverse dynamics controller. Finally, the four proposed neural network controllers are implemented on a single flexible-link experimental test-bed. The neural networks are trained and employed as online controllers. Experimental and simulation results are presented to illustrate the advantages and improved performance of the proposed tip position tracking controllers over the conventional PD-type controllers in the presence of unmodeled dynamics such as hub friction and stiction and payload variations.