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A Backstepping Controller with the RBF Neural Network for Folding-Boom Aerial Work Platform

Author

Listed:
  • Haidong Hu
  • Yandong Song
  • Pu Fan
  • Chen Diao
  • Ning Cai
  • Dan SeliÅŸteanu

Abstract

Aerial work platform is a kind of engineering vehicle which is used for hoisting personnel to the appointed place for maintenance or installation. Based on the dynamics model considering the flexible deformation existing in the arm system of folding-boom aerial platform vehicle, this study presents a NN-based backstepping controller used for trajectory tracking control of work platform. The proposed controller can reduce tracking error of work platform and suppress the vibration simultaneously by using the RBF neural network system to compensate model uncertainties and disturbances. Furthermore, we prove that the whole system is stable and convergent by Lyapunov stability theorem. In addition, we give the simulation results which show that the good control performance of the designed controller for trajectory tracking and vibration inhabiting of work platform in the case of model uncertainties.

Suggested Citation

  • Haidong Hu & Yandong Song & Pu Fan & Chen Diao & Ning Cai & Dan SeliÅŸteanu, 2022. "A Backstepping Controller with the RBF Neural Network for Folding-Boom Aerial Work Platform," Complexity, Hindawi, vol. 2022, pages 1-9, January.
    • Handle: RePEc:hin:complx:4289111
    DOI: 10.1155/2022/4289111
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