Abstract
Adaptive control of a robot manipulator with a passive joint (which has neither an actuator nor a holding brake) is investigated. With the aim to shape the controlled manipulator dynamics to be of minimized motion tracking errors and joint accelerations, we employ a linear quadratic regulator (LQR) optimization technique to obtain an optimal reference model. Adaptive neural network (NN) control has been developed to ensure the reference model can be matched in finite time, in the presence of various uncertainties.
This work is supported by the Marie Curie International Incoming Fellowship H2R Project (FP7-PEOPLE-2010-IIF-275078), the Natural Science Foundation of China under Grants (60804003, 61174045, 61111130208), the International Science and Technology Cooperation Program of China (0102011DFA10950), and the Fundamental Research Funds for the Central Universities (2011ZZ0104).
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Yang, C., Li, Z., Li, J., Smith, A. (2012). Adaptive Neural Network Control of Robot with Passive Last Joint. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7508. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33503-7_12
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DOI: https://doi.org/10.1007/978-3-642-33503-7_12
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