A design framework for iterative learning control (ILC) based on 2-dimensional model predictive control (2D-MPC)

  • Authors:

  • Jia Shi;Furong Gao;Qingying Jiang;Zikai Cao

  • Affiliations:

  • Department of Chemical & Biochemical Engineering, School of Chemistry & Chemical Engineering, Xiamen University, Fujian, P.R.C.;Department of Chemical and Biomolecular Engineering, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong;Department of Chemical & Biochemical Engineering, School of Chemistry & Chemical Engineering, Xiamen University, Fujian, P.R.C.;Department of Chemical & Biochemical Engineering, School of Chemistry & Chemical Engineering, Xiamen University, Fujian, P.R.C.

  • Venue:
  • CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
  • Year:
  • 2009

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Abstract

According to the philosophy of model predictive control (MPC), 2-dimensional (2D) MPC algorithm has been developed for the 2D system. By transforming the ILC design problem into 2D-MPC design problem, a design framework for ILC scheme, referred as 2D-MPILC, has been proposed for the repetitive processes with 2D dynamics. The major advantages of the proposed design framework is the 2D dynamics of the process and the dynamics of the cycle-varying set-point profile can be take into account in the design resulting in a time-wise feedback control and a cycle-wise high-order ILC law combined and optimized in 2D sense. The simulation results demonstrate the effectiveness and robustness of the proposed ILC scheme.