Skip to main content
SpringerLink
Log in
Book cover

Advanced, Contemporary Control pp 1067–1078Cite as

Fast Nonlinear Model Predictive Control Algorithm with Neural Approximation for Embedded Systems: Preliminary Results

  • Conference paper
  • First Online:

  • 1251 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1196))

Abstract

This work presents preliminary results of research concerned with a fast nonlinear Model Predictive Control (MPC) algorithm implemented in an embedded system. In order to obtain a computationally efficient solution, a linear approximation of the predicted trajectory of the controlled variables is calculated for each sampling instant on-line which leads to a quadratic optimisation problem. Furthermore, the matrix of derivatives, which defines the linearised trajectory, is not determined analytically, but it is calculated (approximated) by a specially trained neural network. In order to show effectiveness of the discussed approach, a dynamic process with two inputs and two outputs is considered for which not only simulation results, but also results of real experiments performed in an embedded system based on a microcontroller are given.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Chaber, P., Ławryńczuk, M.: Fast analytical model predictive controllers and their implementation for STM32 ARM microcontroller. IEEE Trans. Industr. Inf. 15, 4580–4590 (2019)

    Article  Google Scholar 

  2. Chaber, P., Ławryńczuk, M.: AutoMATiC: Code generation of model predictive control algorithms for microcontrollers. IEEE Trans. Industr. Inf. 16(7), 4547–4556 (2020). https://doi.org/10.1109/TII.2019.2946842

  3. Grosso, J.M., Ocampo-Martinez, C., Puig, V.: Reliability-based economic model predictive control for generalised flow-based networks including actuators’ health-aware capabilities. Int. J. Appl. Math. Comput. Sci. 26, 361–654 (2016)

    Article  MathSciNet  Google Scholar 

  4. Janczak, A., Korbicz, J.: Two-stage instrumental variables identification of polynomial Wiener Systems with invertible nonlinearities. Int. J. Appl. Math. Comput. Sci. 29, 571–580 (2019)

    Article  MathSciNet  Google Scholar 

  5. Janczak, A.: Identification of Nonlinear Systems Using Neural Networks and Polynomial Models. A Block-Oriented Approach. Lecture Notes in Control and Information Sciences, vol. 310. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  6. Ławryńczuk, M.: Computationally Efficient Model Predictive Control Algorithms: A Neural Network Approach. Studies in Systems, Decision and Control, vol. 3. Springer, Cham (2014)

    Google Scholar 

  7. Ławryńczuk, M.: Explicit nonlinear predictive control algorithms with neural approximation. Neurocomputing 129, 570–584 (2014)

    Article  Google Scholar 

  8. Pour, F.K., Puig, V., Ocampo-Martinez, C.: Multi-layer health-aware economic predictive control of a pasteurization pilot plant. Int. J. Appl. Math. Comput. Sci. 28, 97–110 (2018)

    Article  MathSciNet  Google Scholar 

  9. Stellato, B., Banjac, G., Goulart, P., Bemporad, A., Boyd, S.: OSQP: an operator splitting solver for quadratic programs. arXiv e-prints https://arxiv.org/abs/1711.08013 (2017)

  10. Tatjewski, P.: Advanced Control of Industrial Processes. Structures and Algorithms. Springer, London (2007)

    MATH  Google Scholar 

  11. Wojtulewicz, A., Ławryńczuk, M.: Implementation of multiple-input multiple-output dynamic matrix control algorithm for fast processes using field programmable gate array. IFAC-PapersOnLine 51, 324–329 (2018)

    Article  Google Scholar 

  12. Wojtulewicz, A., Ławryńczuk, M.: Computationally efficient implementation of dynamic matrix control algorithm for very fast processes using programmable logic controller. In: 2018 23rd International Conference on Methods & Models in Automation & Robotics (MMAR), pp. 579–584 (2018)

    Google Scholar 

  13. Zhou, F., Peng, H., Zhang, G., Zeng, X.: A robust controller design method based on parameter variation rate of RBF-ARX model. IEEE Access 7, 160284–160294 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665, Warsaw, Poland

    Patryk Chaber

Authors
  1. Patryk Chaber
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patryk Chaber .

Editor information

Editors and Affiliations

  1. Institute of Automatic Control, Lodz University of Technology, Lodz, Poland

    Andrzej Bartoszewicz

  2. Institute of Automatic Control, Lodz University of Technology, Lodz, Poland

    Jacek Kabziński

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chaber, P. (2020). Fast Nonlinear Model Predictive Control Algorithm with Neural Approximation for Embedded Systems: Preliminary Results. In: Bartoszewicz, A., Kabziński, J., Kacprzyk, J. (eds) Advanced, Contemporary Control. Advances in Intelligent Systems and Computing, vol 1196. Springer, Cham. https://doi.org/10.1007/978-3-030-50936-1_89

Download citation

Publish with us

Policies and ethics

Search

Navigation