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Lead-Lag Controller-Based Iterative Learning Control Algorithms for 3D Crane Systems

  • Radu-Emil PrecupEmail author
  • Florin-Cristian Enache
  • Mircea-Bogdan Rădac
  • Emil M. Petriu
  • Stefan Preitl
  • Claudia-Adina Dragoş
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 2)

Abstract

This chapter deals with the application of two Iterative Learning Control (ILC) structures to the position control of 3D crane systems. The control system structures are based on Cascade Learning (CL) and Previous and Current Cycle Learning (PCCL) which improve the control system performance with frequency domain designed lead-lag controllers for the x-axis and for the y-axis. The parameters of continuous-time real PD learning rules which are also implemented in real-world applications as lead-lag controllers are set such that to fulfill the convergence conditions of CL and PCCL. Elements of anti-swing control for the PCCL structure are discussed. Experimental results are given to solve the crane position control problem of a 3D crane system laboratory equipment.

Keywords

Iterative Learn Control Overhead Crane Crane System Cuckoo Optimization Algorithm Control System Structure 
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 2013

Authors and Affiliations

  • Radu-Emil Precup
    • 1
    Email author
  • Florin-Cristian Enache
    • 1
  • Mircea-Bogdan Rădac
    • 1
  • Emil M. Petriu
    • 2
  • Stefan Preitl
    • 1
  • Claudia-Adina Dragoş
    • 1
  1. 1.Department of Automation and Applied Informatics“Politehnica” University of TimisoaraTimisoaraRomania
  2. 2.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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