Immunological mechanism inspired iterative learning control

Abstract In this paper, an iterative learning control method based on the recognition, response, and memory mechanism of immune system (IRRM-ILC) is proposed. According to the immunological recognition, response, and memory processes, several new components are designed and then combined with the conventional ILC. The proposed IRRM-ILC controller can mitigate the disturbances by detecting, identifying, and memorizing them. Thereby, the anti-disturbance ability of the conventional ILC is enhanced. The proposed IRRM-ILC is applied to a temperature control system of wet spinning coagulation process. Simulation results with random and repeated disturbances demonstrate that the IRRM-ILC has a better performance than the conventional ILC in terms of convergence and stability in the presence of disturbances.

[1]  Rolf Unbehauen,et al.  Iterative learning neural network control for nonlinear system trajectory tracking , 2000, ESANN.

[2]  David H. Owens,et al.  Iterative learning control - An optimization paradigm , 2015, Annu. Rev. Control..

[3]  Li Feng.  APPLICATION OF AN ON-LINE CONTROL OF COAGULATION BATH CONCENTRATION IN PAN PRECURSOR PRODUCTION , 2012 .

[4]  Huijun Gao,et al.  Finite-Horizon $H_{\infty} $ Filtering With Missing Measurements and Quantization Effects , 2013, IEEE Transactions on Automatic Control.

[5]  Richard W. Longman,et al.  Simple learning control made practical by zero-phase filtering: applications to robotics , 2002 .

[6]  Dimitry Gorinevsky,et al.  Distributed Loop-shaping for Iterative Control of Batch Processes , 2002 .

[7]  Jay H. Lee,et al.  Experimental application of a quadratic optimal iterative learning control method for control of wafer temperature uniformity in rapid thermal processing , 2003 .

[8]  Ruan Xiao-e,et al.  Retrospective review of some iterative learning control techniques with a comment on prospective long-term learning , 2012 .

[9]  Paul Lewin,et al.  Practical implementation of a real-time iterative learning position controller , 2000 .

[10]  Li Xiaoli,et al.  Multiple model iterative learning control , 2010 .

[11]  M. Cooper,et al.  The evolution of adaptive immunity. , 2006, Annual review of immunology.

[12]  Hong Wang,et al.  Complex Stochastic Systems Modeling and Control via Iterative Machine Learning , 2006, 2006 IEEE International Conference on Engineering of Intelligent Systems.

[13]  Andrew G. Alleyne,et al.  Nonlinear control of an electrohydraulic injection molding machine via iterative adaptive learning , 1999 .

[14]  Ali Shokuhfar,et al.  The effect of commercial polyacrylonitrile fibers characterizations on the produced carbon fibers properties , 2008 .

[15]  Yongsheng Ding,et al.  An Intelligent Cooperative Decoupling Controller for Coagulation Bath in Polyacrylonitrile Carbon Fiber Production , 2013, IEEE Transactions on Control Systems Technology.

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

[17]  Mikael Norrlöf,et al.  An adaptive iterative learning control algorithm with experiments on an industrial robot , 2002, IEEE Trans. Robotics Autom..

[18]  Cheng Shao,et al.  Robust iterative learning control with applications to injection molding process , 2001 .

[19]  S. Saab.  A stochastic iterative learning control algorithm with application to an induction motor , 2004 .

[20]  Madhukar Pandit,et al.  Optimizing iterative learning control of cyclic production processes with application to extruders , 1999, IEEE Trans. Control. Syst. Technol..

[21]  Kevin L. Moore,et al.  Iterative Learning Control: Brief Survey and Categorization , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

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

[23]  Boutaieb Dahhou,et al.  Application of iterative learning control to an exothermic semibatch chemical reactor , 2002, IEEE Trans. Control. Syst. Technol..

[24]  Xu Jian-Xin,et al.  On Learning Control: The State of the Art and Perspective , 2005 .

[25]  Zhaohui Chen,et al.  Influence of heat treatment on physical–chemical properties of PAN-based carbon fiber , 2006 .

[26]  A.G. Alleyne,et al.  A survey of iterative learning control , 2006, IEEE Control Systems.

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