Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory

The convergence analysis of two-dimensional based integrated predictive iterative learning control (2D-IPILC) is presented for batch process in presence of output noises. In the 2D-IPILC method, iterative learning control (ILC) in the batch domain is integrated reasonably with real-time model predictive control (MPC) in the time domain. Based on the 2D system theory, system response of output tracking error is described explicitly by using the state-transition matrix of the 2D system for the first time. Then influence of output noise to the tracking error can be revealed clearly. By using this description of model response, convergence properties of the 2D-IPILC can be also analyzed theoretically. The sufficient convergence conditions of output tracking error in the proposed algorithm are derived for a class of linear systems. Simulation results have demonstrated the effectiveness of the proposed method.

[1]  Jay H. Lee,et al.  Model predictive control technique combined with iterative learning for batch processes , 1999 .

[2]  J. R. Cueli,et al.  Iterative nonlinear model predictive control. Stability, robustness and applications , 2008 .

[3]  Jay H. Lee,et al.  Iterative learning control-based batch process control technique for integrated control of end product properties and transient profiles of process variables , 2003 .

[4]  Francis J. Doyle,et al.  Survey on iterative learning control, repetitive control, and run-to-run control , 2009 .

[5]  Jay H. Lee,et al.  Convergence of constrained model-based predictive control for batch processes , 2000, IEEE Trans. Autom. Control..

[6]  Zhihua Xiong,et al.  Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory , 2014 .

[7]  Tommy W. S. Chow,et al.  Iterative learning control for linear time-variant discrete systems based on 2-D system theory , 2005 .

[8]  Youqing Wang,et al.  Iterative learning model predictive control for multi-phase batch processes , 2008 .

[9]  T. Kaczorek.  Two-Dimensional Linear Systems , 1985 .

[10]  Zhihua Xiong,et al.  2-D theory based integrated predictive iterative learning control for batch process , 2013, 2013 10th IEEE International Conference on Control and Automation (ICCA).

[11]  J. Kurek,et al.  Iterative learning control synthesis based on 2-D system theory , 1993, IEEE Trans. Autom. Control..

[12]  Yuan Yao,et al.  Two-time dimensional dynamic matrix control for batch processes with convergence analysis against the 2D interval uncertainty , 2012 .

[13]  F. Doyle,et al.  Model predictive control with learning‐type set‐point: Application to artificial pancreatic β‐cell , 2010 .

[14]  Jay H. Lee,et al.  ITERATIVE LEARNING CONTROL APPLIED TO BATCH PROCESSES: AN OVERVIEW , 2006 .

[15]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[16]  J. R. Cueli.  Iterative Nonlinear Model Predictive Control , 2013 .

[17]  Tie-Jun Wu,et al.  Robust iterative learning control design for batch processes with uncertain perturbations and initialization , 2006 .

[18]  Suguru Arimoto,et al.  Bettering operation of Robots by learning , 1984, J. Field Robotics.

[19]  D. H. Owens,et al.  Iterative learning control: quantifying the effect of output noise , 2011 .