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Iterative learning control of multivariable uncertain nonlinear systems with nonrepetitive trajectory

Authors
  • Boudjedir, Chems Eddine1
  • Boukhetala, Djamel1
  • Bouri, Mohamed2
  • 1 Ecole Nationale Polytechnique (ENP), 10 Rue des Freres OUDEK, El-Harrach, Algiers, 16200, Algeria , Algiers (Algeria)
  • 2 Ecole Polytechnique Fédérale de Lausanne (EPFL), Station 9, Lausanne, 1015, Switzerland , Lausanne (Switzerland)
Type
Published Article
Journal
Nonlinear Dynamics
Publisher
Springer-Verlag
Publication Date
Dec 11, 2018
Volume
95
Issue
3
Pages
2197–2208
Identifiers
DOI: 10.1007/s11071-018-4685-0
Source
Springer Nature
Keywords
License
Yellow

Abstract

Iterative learning control (ILC) theory is based on the traditional assumptions of resetting condition and repetitive trajectory. To overcome these restrictions, a novel ILC is developed in this paper for MIMO uncertain nonlinear systems subject to external disturbances and performing nonrepetitive trajectory. The proposed ILC scheme works under alignment condition and nonrepetitive trajectory that can be varied from iteration to iteration in time interval length, in magnitude scale as well as in initial and final positions. A time-scale transformation is introduced and combined with Lyapunov method to synthesise the control law and to prove the asymptotic convergence. The tracking error converges to zero as the number of iterations increases. Simulation of pick-and-place operations is carried out on a parallel Delta robot in order to show the feasibility and the effectiveness of the proposed approach.

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