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Nonlinear internal model control using neural networks: an application for machining processes

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Abstract

This paper presents a novel approach to the control of the cutting force on the basis of the internal model control (IMC) principle. The main goal is to control a single output variable, the cutting force, by changing a single input variable, the feedrate. A neural model is used as an internal model to determine the control inputs (feedrate) necessary to keep the cutting force constant. Three approaches, the fuzzy logic controller (FLC), the direct inverse controller (DIC) and the IMC, based on artificial neural networks (IMC-NN), are simulated and their performances are assessed in terms of several performance measurements. The results demonstrate that IMC-NN strategy provides a better disturbance rejection than FLC for the cases analysed.

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References

  1. Hornik K, Stincheombe M and White H (1989) Multilayer feedforward networks are universal approximators. Neur Ntwks 2:359–366

    Article  Google Scholar 

  2. Hunt KJ, Sbarbaro D, Zbikowski R and Gawthrop PJ (1992) Neural networks for control systems—a survey. Automatica 28(6):1083–1112

    Article  MathSciNet  MATH  Google Scholar 

  3. Haber RE, Haber RH, Ros S, Alique A and Alique JR (2002) Dynamic model of the machining process on the basis of neural networks: from simulation to real time application. Lect N Comp Sci 2331:574–583

    Google Scholar 

  4. Hagan MT, Demuth HB and De Jesús O (2002) An introduction to the use of neural networks in control systems. Int J Rob Non Contr 12:959–985

    Article  MATH  Google Scholar 

  5. Morari M, Zafiriou E (1989) Robust process control. Prentice-Hall, Englewood Cliffs, NJ

  6. Lightbody G, Irwin GW (1997) Nonlinear control structures based on embedded neural systems models. IEEE Trans Neur Ntwks 8(3):553–567

    Article  Google Scholar 

  7. Kambhampati C, Craddock RJ, Tham M and Warwick K (2000) Inverse model control using recurrent networks. Math Comp Simul 51:181–199

    Article  Google Scholar 

  8. Carotenuto R (2001) An iterative system inversion technique. Int J Adapt Contr Sig Process 15:85–91

    Article  MATH  Google Scholar 

  9. Lauderbaugh LK, Ulsoy AG (1989) Model reference adaptive force control in milling. ASME J Engin Industr 111:13–21

    Google Scholar 

  10. Rober SJ, Shin YC (1996) Control of cutting force for milling processes using an extended model reference adaptive control scheme. J Manufact Sci Engin 118:339–347

    Google Scholar 

  11. Landers R, Ulsoy A (2000) Model-based machining control. J Dynam Sys Measur Contr 122(3):521–527

    Article  Google Scholar 

  12. Haber RE, Haber RH, Alique A and Ros S (2002) Application of knowledge-based systems for supervision and control of machining processes. In: Chang SK (ed) Handbook of software engineering and knowledge engineering vol. II, World Scientific Publishing, Singapore

  13. Norgard M, Ravn O, Poulsen NK and Hansen LK (2000) Neural networks for modelling and control of dynamics systems. Springer, Berlin Heidelberg New York

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Authors and Affiliations

  1. Instituto de Automática Industrial (CSIC), km 22, 800 N-III, La Poveda, 28500, Madrid, Spain

    Rodolfo E. Haber & J. R. Alique

  2. Escuela Técnica Superior, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, Ctra. de Colmenar Viejo, km 15, 28049, Madrid, Spain

    Rodolfo E. Haber

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  1. Rodolfo E. Haber
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  2. J. R. Alique
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Correspondence to Rodolfo E. Haber.

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Haber, R.E., Alique, J.R. Nonlinear internal model control using neural networks: an application for machining processes. Neural Comput & Applic 13, 47–55 (2004). https://doi.org/10.1007/s00521-003-0394-8

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  • DOI: https://doi.org/10.1007/s00521-003-0394-8

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