Enhancing design of visual-servo delayed system

Zhi Ren Tsai, Yau Zen Chang*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review


A robust adaptive predictor is proposed to solve the time-varying and delay control problem of an overhead crane system with a stereo-vision servo. The predictor is based on the use of a recurrent neural network (RNN) with tapped delays, and is used to supply the real-time signal of the swing angle. There are two types of discrete-time controllers under investigation, i.e., the proportional-integral-derivative (PID) controller and the sliding controller. Firstly, a design principle of the neural predictor is developed to guarantee the convergence of its swing angle estimation. Then, an improved version of the particle swarm optimization algorithm, the parallel particle swarm optimization (PPSO) method is used to optimize the control parameters of these two types of controllers. Finally, a homemade overhead crane system equipped with the Kinect sensor for the visual servo is used to verify the proposed scheme. Experimental results demonstrate the effectiveness of the approach, which also show the parameter convergence in the predictor.

Original languageEnglish
Pages (from-to)232-240
Number of pages9
JournalJournal of Electronic Science and Technology
Issue number3
StatePublished - 2018

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  • Parallel particle swarm optimization
  • Robust adaptive predictor
  • Stereo-vision servo
  • Time-varying delay


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