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

32 Citations (Scopus)

Abstract

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
Volume13
Issue number1
DOIs
Publication statusPublished - 2008 Dec 1

Keywords

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

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Vibration control of load for rotary crane system using neural network with GA-based training'. Together they form a unique fingerprint.

Cite this