Iterative Learning - MPC: an Alternative Strategy

A repetitive system is one that continuously repeats a finite-duration procedure (operation) along the time. This kind of systems can be found in several industrial fields such as robot manipulation (Tan, Huang, Lee & Tay, 2003), injection molding (Yao, Gao & Allgower, 2008), batch processes (Bonvin et al., 2006; Lee & Lee, 1999; 2003) and semiconductor processes (Moyne, Castillo, & Hurwitz, 2003). Because of the repetitive characteristic, these systems have two count indexes or time scales: one for the time running within the interval each operation lasts, and the other for the number of operations or repetitions in the continuous sequence. Consequently, it can be said that a control strategy for repetitive systems requires accounting for two different objectives: a short-term disturbance rejection during a finite-duration single operation in the continuous sequence (this frequently means the tracking of a predetermined optimal trajectory) and the long-term disturbance rejection from operation to operation (i.e., considering each operation as a single point of a continuous process1). Since in essence, the continuous process basically repeats the operations (assuming that long-term disturbances are negligible), the key point to develop a control strategy that accounts for the second objective is to use the information from previous operations to improve the tracking performance of the future sequence.

[1]  T. Sugie,et al.  On iterative learning control , 2004, International Conference on Informatics Research for Development of Knowledge Society Infrastructure, 2004. ICKS 2004..

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

[3]  E. J. Adam.  Adaptive Iterative Learning Control Applied to Nonlinear Batch Reactor , 2007 .

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

[5]  Alejandro H. González,et al.  Conditions for offset elimination in state space receding horizon controllers: A tutorial analysis , 2008 .

[6]  E. Mosca.  MODEL-BASED PREDICTIVE CONTROL , 2006 .

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

[8]  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 .

[9]  Jacinto L. Marchetti,et al.  Infinite Horizon MPC applied to batch processes. Part II , 2009 .

[10]  Furong Gao,et al.  Barrel temperature control during operation transition in injection molding , 2008 .

[11]  Mikael Norrlöf,et al.  Iterative Learning Control : Analysis, Design, and Experiments , 2000 .

[12]  Marek B. Zaremba,et al.  Robust iterative learning control design is straightforward for uncertain LTI systems satisfying the robust performance condition , 2003, IEEE Trans. Autom. Control..

[13]  Mo Jamshidi,et al.  Learning control of robot manipulators , 1992 .

[14]  D. de Roover,et al.  Synthesis of a robust iterative learning controller using an H/sub /spl infin// approach , 1996 .

[15]  James Moyne,et al.  Run-to-Run Control in Semiconductor Manufacturing , 2000 .

[16]  Dominique Bonvin,et al.  Controllability and stability of repetitive batch processes , 2007 .

[17]  Myung Jin Chung,et al.  A Robust Approach to Iterative Learning Control Design for Uncertain Systems , 1998, Autom..

[18]  Jay H. Lee,et al.  Model predictive control for nonlinear batch processes with asymptotically perfect tracking , 1997 .

[19]  Jay H. Lee,et al.  Model-based iterative learning control with a quadratic criterion for time-varying linear systems , 2000, Autom..

[20]  Myung Jin Chung,et al.  Robust ILC and current feedback for uncertain linear systems , 1998 .

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

[22]  Marko Bacic,et al.  Model predictive control , 2003 .

[23]  Darci Odloak,et al.  Stable IHMPC for Unstable Systems , 2008 .

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

[25]  Bala Srinivasan,et al.  IMPROVEMENT OF PROCESS OPERATION IN THE PRODUCTION OF SPECIALTY CHEMICALS , 2006 .

[26]  Arthur Tay,et al.  Disturbance compensation incorporated in predictive control system using a repetitive learning approach , 2007, Syst. Control. Lett..

[27]  Jian-Xin Xu,et al.  Iterative Learning Control , 1998 .

[28]  James B. Rawlings,et al.  Discrete-time stability with perturbations: application to model predictive control , 1997, Autom..