Abstract
Performance of a neural network-based control scheme is investigated for uncalibrated robotic hand-eye coordination system. Since the conditions for offline modelling with neural network are normally different from those for online control, unmodeled dynamics is inevitable and should be compensated by controller design. We analyze the system’s tracking error and stability with a discrete system model under a PI visual servoing controller, taking account of robot dynamics and image processing delays. The internal model principle is adopted to arrive at a feedforward compensator to enhance system performance.
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Su, J. (2005). Performance Analysis of Neural Network-Based Uncalibrated Hand-Eye Coordination. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_35
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DOI: https://doi.org/10.1007/11427469_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
eBook Packages: Computer ScienceComputer Science (R0)