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. © Springer-Verlag Berlin Heidelberg 2012.
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Yang, C., Li, Z., Li, J., & Smith, A. (2012). Adaptive neural network control of robot with passive last joint. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7508 LNAI, pp. 113–122). https://doi.org/10.1007/978-3-642-33503-7_12
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