International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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LMI-Based Synthesis of Robust Iterative Learning Controller with Current Feedback for Linear Uncertain Systems

  • Xu, Jianming (College of Information Engineering, Zhejiang University of Technology) ;
  • Sun, Mingxuan (College of Information Engineering, Zhejiang University of Technology) ;
  • Yu, Li (College of Information Engineering, Zhejiang University of Technology)
  • Published : 2008.04.30
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Abstract

This paper addresses the synthesis of an iterative learning controller for a class of linear systems with norm-bounded parameter uncertainties. We take into account an iterative learning algorithm with current cycle feedback in order to achieve both robust convergence and robust stability. The synthesis problem of the developed iterative learning control (ILC) system is reformulated as the ${\gamma}$-suboptimal $H_{\infty}$ control problem via the linear fractional transformation (LFT). A sufficient convergence condition of the ILC system is presented in terms of linear matrix inequalities (LMIs). Furthermore, the ILC system with fast convergence rate is constructed using a convex optimization technique with LMI constraints. The simulation results demonstrate the effectiveness of the proposed method.

Keywords

References

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