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Performance Analysis of Neural Network-Based Uncalibrated Hand-Eye Coordination

  • Jianbo Su
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

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.

Keywords

Tracking Error Visual Servoing Root Locus Robot Hand Feedforward Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jianbo Su
    • 1
  1. 1.Department of AutomationShanghai Jiao Tong UniversityShanghaiChina

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