Zusammenfassung: | |
An adaptive learning algorithm using an artificial neural network (ANN) has been proposed to predict the passive joint position of under-actuated robot manipulator. In this approach, a specific ANN model has been designed and trained to learn a desired set of joint angular positions for the passive joint from a given set of input torque and angular position for the active joint over a certain period of time. Trying to overcome the disadvantages of many used techniques in the literature, the ANNs have a significant advantage of being a model-free method. The learning algorithm can directly determine the position of its passive joint, and can, therefore, completely eliminate the need for any system modelling. Even though it is very difficult in practice, data used in this study were recorded experimentally from sensors fixed on robot's joints to overcome the effect of kinematics uncertainties present in the real world such as ill-defined linkage parameters and backlashes in gear trains. An ANN was trained using the experimentally obtained data and then used to predict the path of the passive joint that is positioned by the dynamic coupling of the active joint. The generality and efficiency of the proposed algorithm are demonstrated through simulations of an under-actuated robot manipulator; finally, the obtained results were successfully verified experimentally. © Authors 2011.
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Lizenzbestimmungen: | Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden. Dieser Beitrag ist aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich. |
Publikationstyp: | Article |
Publikationsstatus: | publishedVersion |
Erstveröffentlichung: | 2011 |
Schlagwörter (englisch): | Artificial neural network, Prediction algorithm, Under-actuated robot, Adaptive learning algorithm, Angular positions, Artificial Neural Network, Dynamic couplings, Gear train, Model-free method, Passive joints, Prediction algorithms, Robot manipulator, System modelling, Adaptive algorithms, Flexible manipulators, Forecasting, Industrial robots, Learning algorithms, Modular robots, Robot applications, Neural networks |
Fachliche Zuordnung (DDC): | 621 | Angewandte Physik |
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