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PD Control of Overhead Crane Systems with Neural Compensation

  • Rigoberto Toxqui Toxqui
  • Wen Yu
  • Xiaoou Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

This paper considers the problem of PD control of overhead crane in the presence of uncertainty associated with crane dynamics. By using radial basis function neural networks, these uncertainties can be compensated effectively. This new neural control can resolve the two problems for overhead crane control: 1) decrease steady-state error of normal PD control. 2) guarantee stability via neural compensation. By Lyapunov method and input-to-state stability technique, we prove that these robust controllers with neural compensators are stable. Real-time experiments are presented to show the applicability of the approach presented in this paper.

Keywords

Tracking Error Radial Basis Function Neural Network Model Reference Adaptive Control Gravity Compensation Overhead Crane 
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 2006

Authors and Affiliations

  • Rigoberto Toxqui Toxqui
    • 1
  • Wen Yu
    • 1
  • Xiaoou Li
    • 2
  1. 1.Departamento de Control AutomáticoCINVESTAV-IPNMéxico D.F.México
  2. 2.Sección de Computación, Departamento de Ingeniería EléctricaCINVESTAV-IPNMéxico D.F.México

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