Skip to main content

Nash-Based Distributed MPC for Multi-Rate Systems

  • Chapter
  • First Online:
Distributed Model Predictive Control Made Easy

Abstract

In this chapter, a new Nash-based distributed MPC method is proposed to control large-scale multi-rate systems with linear dynamics that are coupled via inputs. These systems are multi-rate systems in the sense that either output measurements or input updates are not available at certain sampling times. Such systems can arise when the number of sensors is less than the number of variables to be controlled or when measurements of outputs cannot be completed simultaneously because of applicational limitations. The multi-rate nature gives rise to a lack of information which will cause uncertainty in the system’s performance. To compensate for the information loss due to the multi-rate nature of the systems under study, a distributed Kalman filter is proposed to provide an optimal estimate of the missing information.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

Notes

  1. 1.

    Since the control horizon constraint only holds for \(\Delta \mathbf{u}_i\) via \({{\tilde{\varvec{\theta }}}}_i\) and not for \(\mathbf{v}_i\), the assumption is made that the process noise is zero from \(t+N_\mathrm{c}-1\) on.

References

  1. J.H. Lee, M.S. Gelormino, M. Morari, Model predictive control of multi-rate sampled-data systems: a state-space approach. Int. J. Control 55(1), 153–191 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  2. M. Embiruçu, C. Fontes, Multirate multivariable generalized predictive control and its application to a slurry reactor for ethylene polymerization. Chem. Eng. Sci 61(17), 5754–5767 (2006)

    Article  Google Scholar 

  3. R.S. Gopinath, B.W. Bequettelt, R.J. Roy, H. Kaufman, Multirate MPC design for a nonlinear drug infusion system, in Proceedings of the American Control Conference, pp. 102–106 (Baltimore, Maryland, June–July 1994)

    Google Scholar 

  4. M. Ohshima, I. Hashimoto, H. Ohno, M. Takeda, T. Yoneyama, F. Gotoh, Multirate multivariable model predictive control and its application to a polymerization reactor. Int. J. Control 59(3), 731–742 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  5. R. Scattolini, N. Schiavoni, A multi-rate model based predictive control. IEEE Trans. Autom. Control 40(6), 1093–1097 (June 1995)

    Article  MathSciNet  MATH  Google Scholar 

  6. R.R. Negenborn, B. De Schutter, J. Hellendoorn, Multi-agent model predictive control for transportation networks: Serial versus parallel schemes. Eng. Appl. Artif. Intell. 21(3), 353–366 (2008)

    Article  Google Scholar 

  7. R.R. Negenborn, Z. Lukszo, H. Hellendoorn, Intelligent Infrastructures (Springer, Dordrecht, 2010)

    Book  MATH  Google Scholar 

  8. A.N. Venkat, I.A. Hiskens, J.B. Rawlings, S.J. Wright, Distributed MPC strategies with application to power system automatic generation control. IEEE Trans. Control Syst. Technol. 16(6), 1192–1206 (2008)

    Article  Google Scholar 

  9. S. Wang, E.J. Davison, On the stabilization of decentralized control systems. IEEE Trans. Autom. Control 18(5):473–478 (1973)

    Google Scholar 

  10. V.D. Blondel, J.N. Tsitsiklis, A survey of computational complexity results in systems and control. Automatica 36(9):1249–1274 (2000)

    Google Scholar 

  11. A.N. Venkat, J.B. Rawlings, S.J. Wright, Stability and optimality of distributed model predictive control, in 44th IEEE Conference on Decision and Control, and the European Control Conference (Seville, Spain, 12–15 December 2005)

    Google Scholar 

  12. W. Al-Gherwi, H. Budman, A. Elkamel, Selection of control structures for distributed model predictive control in the presence of model errors. J. Process Control 20(3), 270–284 (2010)

    Article  Google Scholar 

  13. S. Li, Y. Zhang, Q. Zhu, Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem. Inf. Sci. 170(2–4), 329–349 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  14. L. Giovanini, J. Balderud, Game approach to distributed model predictive control, in Proceedings of the International Control Conference (Glasgow, UK, 2006)

    Google Scholar 

  15. S. Roshany-Yamchi, M. Cychowski, R.R. Negenborn, B. De Schutter, K. Delaney, J. Connell, Kalman filter-based distributed predictive control of large-scale multi-rate systems: Application to power networks. IEEE Trans. Control Syst. Technol. 99:1–13 (2011)

    Google Scholar 

  16. S. Roshany-Yamchi, R. R. Negenborn, M. Cychowski, B. De Schutter, J. Connell, K. Delaney, Distributed model predictive control and estimation of large-scale multi-rate systems, in Proceedings of the 18th IFAC World Congress (Milano, Italy, August 28–September 2 2011)

    Google Scholar 

  17. S. Roshany-Yamchi, A Distributed Model Predictive Control For Multi-Agent Multi-Rate Systems, Ph.D. thesis, Cork Institute of Technology, Cork, Ireland, 2012

    Google Scholar 

  18. S. Roshany-Yamchi, A.A. Cornelio, K. Delaney, J. Connell, An application of nash game to distributed multi-rate predictive control, in Proceedings of the 14th IASTED International Conference on Control and Applications (Crete, Greece, 18–20 June 2012)

    Google Scholar 

Download references

Acknowledgments

This research is supported by the Irish Programme for Research in Third Level Institutions (Cycle 4) (funded under the National Development Plan 2007-2013 with assistance from the European Regional Development Fund) and the VENI project “Intelligent multi-agent control for flexible coordination of transport hubs” (project 11210) of the Dutch Technology Foundation STW, a subdivision of The Netherlands Organisation for Scientific Research (NWO).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Roshany-Yamchi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Roshany-Yamchi, S., Negenborn, R.R., Cornelio, A.A. (2014). Nash-Based Distributed MPC for Multi-Rate Systems. In: Maestre, J., Negenborn, R. (eds) Distributed Model Predictive Control Made Easy. Intelligent Systems, Control and Automation: Science and Engineering, vol 69. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7006-5_21

Download citation

  • DOI: https://doi.org/10.1007/978-94-007-7006-5_21

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-7005-8

  • Online ISBN: 978-94-007-7006-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics