Integrated Iterative Learning Control Strategy for Batch Processes

An integrated iterative learning control strategy based on time-varying perturbation models for batch processes is proposed in this paper. A linear perturbation model is firstly obtained in order to control the perturbation variables rather than the actual process variables themselves. Next, an integrated control strategy which combines ILC with real-time feedback control is used to control the perturbation model. It leads to superior tracking performance and better robustness against disturbance and uncertainty. Lastly, the effectiveness of the proposed method is verified by examples.

[1]  Min-Sen Chiu,et al.  An integrated iterative learning control strategy with model identification and dynamic R-parameter for batch processes , 2013 .

[2]  SU San-mai.  A Hybrid Genetic Algorithm for Constrained Optimization , 2009 .

[3]  Lawrence B. Evans,et al.  A coordinate‐transformation method for the numerical solution of nonlinear minimum‐time control problems , 1975 .

[4]  Tao Liu,et al.  IMC-based iterative learning control for batch processes with uncertain time delay , 2010 .

[5]  Jia Li,et al.  Neuro-fuzzy-based dynamic quadratic criterion-iterative learning control for batch process , 2013 .

[6]  Qiu Min.  Particle Swarm Optimization Algorithm Based Iterative Learning Algorithm for Batch Processes , 2011 .

[7]  Furong Gao,et al.  Stage-based process analysis and quality prediction for batch processes , 2005 .

[8]  Jie Zhang,et al.  Product Quality Trajectory Tracking in Batch Processes Using Iterative Learning Control Based on Time-Varying Perturbation Models , 2003 .

[9]  Tie-Jun Wu,et al.  From Two-Dimensional Linear Quadratic Optimal Control to Iterative Learning Control. Paper 1. Two-Dimensional Linear Quadratic Optimal Controls and System Analysis , 2006 .

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

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

[12]  Si-Zhao Joe Qin,et al.  A two-stage iterative learning control technique combined with real-time feedback for independent disturbance rejection , 2004, Autom..

[13]  Min-Sen Chiu,et al.  Integrated neuro-fuzzy model and dynamic R-parameter based quadratic criterion-iterative learning control for batch process , 2012, Neurocomputing.

[14]  Tie-Jun Wu,et al.  From two-dimensional linear quadratic optimal control to iterative learning control. Paper 2. Iterative learning controls for batch processes , 2006 .

[15]  Eric Rogers,et al.  Stability Analysis for Linear Repetitive Processes , 1992 .

[16]  Dominique Bonvin,et al.  Optimal operation of batch reactors—a personal view , 1998 .

[17]  Jie Zhang,et al.  Integrated tracking control strategy for batch processes using a batch-wise linear time-varying perturbation model , 2007 .

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

[19]  Jing-Sin Liu,et al.  A P-type iterative learning controller for robust output tracking of nonlinear time-varying systems , 1996 .

[20]  Tao Liu,et al.  Advanced PI control with simple learning set-point design: Application on batch processes and robust stability analysis , 2012 .

[21]  Masao Fukushima,et al.  A successive quadratic programming algorithm with global and superlinear convergence properties , 1986, Math. Program..

[22]  Tommy W. S. Chow,et al.  2-D system theory based iterative learning control for linear continuous systems with time delays , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.