Vibration control of load for rotary crane system using neural network with GA-based training

Kunihiko Nakazono, Kouhei Ohnishi, Hiroshi Kinjo, Tetsuhiko Yamamoto

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


A neuro-controller for vibration control of load in a rotary crane system is proposed involving the rotation about the vertical axis only. As in a nonholonomic system, the vibration control method using a static continuous state feedback cannot stabilize the load swing. It is necessary to design a time-varying feedback controller or a discontinuous feedback controller. We propose a simple three-layered neural network as a controller (NC) with genetic algorithm-based (GA-based) training in order to control load swing suppression for the rotary crane system. The NC is trained by a real-coded GA, which substantially simplifies the design of the controller. It appeared that a control scheme with performance comparable to conventional methods can be obtained by a relatively simple approach.

Original languageEnglish
Pages (from-to)98-101
Number of pages4
JournalArtificial Life and Robotics
Issue number1
Publication statusPublished - 2008 Dec 1


  • Genetic algorithm
  • Neural network
  • Rotary crane
  • Vibration control

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • Artificial Intelligence


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