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

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Aspects of Computational Intelligence: Theory and Applications

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.

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Correspondence to Radu-Emil Precup .

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Precup, RE., Enache, FC., Rădac, MB., Petriu, E.M., Preitl, S., Dragoş, CA. (2013). Lead-Lag Controller-Based Iterative Learning Control Algorithms for 3D Crane Systems. In: Madarász, L., Živčák, J. (eds) Aspects of Computational Intelligence: Theory and Applications. Topics in Intelligent Engineering and Informatics, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30668-6_2

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  • DOI: https://doi.org/10.1007/978-3-642-30668-6_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30667-9

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