Skip to main content

Advertisement

Springer Nature Link
Log in

Protecting Systems from Violating Constraints Using Reference Governors

  • Survey Article
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Reference governors are add-on predictive safety supervision algorithms that monitor and modify, if it becomes necessary, commands passed to a nominal system to ensure that pointwise-in-time state and input constraints are satisfied. After briefly surveying the basics of the reference governor schemes, this paper discusses several more recent extensions of the reference governors. These include reduced order reference governors with flexible error budgeting, reference governors for nonlinear systems that exploit the logarithmic norm for response bounding, stochastic reference governors, and controller state and reference governors. Learning reference governors that are capable of handling constraints in uncertain systems are also discussed.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Jaw L, Mattingly J. Aircraft engine controls. Reston: American Institute of Aeronautics and Astronautics; 2009.

    Book  Google Scholar 

  2. Rawlings JB, Mayne DQ, Diehl M. Model predictive control: theory, computation, and design. Madison: Nob Hill Publishing; 2017.

    Google Scholar 

  3. Garone E, Di Cairano S, Kolmanovsky I. Reference and command governors for systems with constraints: a survey on theory and applications. Automatica. 2017;75:306–28.

    Article  MathSciNet  MATH  Google Scholar 

  4. Gilbert EG, Tan KT. Linear systems with state and control constraints: the theory and application of maximal output admissible sets. IEEE Trans Autom Control. 1991;36(9):1008–20.

    Article  MathSciNet  MATH  Google Scholar 

  5. Gilbert EG, Kolmanovsky I. Fast reference governors for systems with state and control constraints and disturbance inputs. Int J Robust Nonlinear Control IFAC Affil J. 1999;9(15):1117–41.

    Article  MathSciNet  MATH  Google Scholar 

  6. Kolmanovsky I, Gilbert EG. Theory and computation of disturbance invariant sets for discrete-time linear systems. Math Probl Eng. 1998;4(4):317–67.

    Article  MATH  Google Scholar 

  7. Gilbert E, Kolmanovsky I. Nonlinear tracking control in the presence of state and control constraints: a generalized reference governor. Automatica. 2002;38(12):2063–73.

    Article  MathSciNet  MATH  Google Scholar 

  8. Miller RH, Kolmanovsky I, Gilbert EG, Washabaugh PD. Control of constrained nonlinear systems: a case study. IEEE Control Syst Mag. 2000;20(1):23–32.

    Article  Google Scholar 

  9. Bemporad A. Reference governor for constrained nonlinear systems. IEEE Trans Autom Control. 1998;43(3):415–9.

    Article  MathSciNet  MATH  Google Scholar 

  10. Garone E, Nicotra MM. Explicit reference governor for constrained nonlinear systems. IEEE Trans Autom Control. 2015;61(5):1379–84.

    Article  MathSciNet  MATH  Google Scholar 

  11. Nicotra MM, Garone E. The explicit reference governor: a general framework for the closed-form control of constrained nonlinear systems. IEEE Control Syst Mag. 2018;38(4):89–107.

    Article  MathSciNet  MATH  Google Scholar 

  12. Liu K, Li N, Rizzo D, Garone E, Kolmanovsky I, Girard A. Model-free learning to avoid constraint violations: an explicit reference governor approach. In: 2019 American control conference (ACC). IEEE. 2019. pp. 934–40.

  13. Meerkov S. Principle of vibrational control: theory and applications. IEEE Trans Autom Control. 1980;25(4):755–62.

    Article  MathSciNet  MATH  Google Scholar 

  14. Garone E, Tedesco F, Casavola A. Sensorless supervision of linear dynamical systems: the feed-forward command governor approach. Automatica. 2011;47(7):1294–303.

    Article  MathSciNet  MATH  Google Scholar 

  15. Kalabić UV, Kolmanovsky IV, Gilbert EG. Reduced order extended command governor. Automatica. 2014;50(5):1466–72.

    Article  MathSciNet  MATH  Google Scholar 

  16. Kalabić U, Kolmanovsky I, Buckland J, Gilbert E. Reduced order reference governor. In: 51st conference on decision and control (CDC). IEEE. 2012. pp. 3245–51.

  17. Di Cairano S, Kalabić UV, Kolmanovsky IV. Reference governor for network control systems subject to variable time-delay. Automatica. 2015;62:77–86.

    Article  MathSciNet  MATH  Google Scholar 

  18. Gilbert EG, Ong C-J. Constrained linear systems with hard constraints and disturbances: an extended command governor with large domain of attraction. Automatica. 2011;47(2):334–40.

    Article  MathSciNet  MATH  Google Scholar 

  19. Li N, Kolmanovsky IV, Girard A. A reference governor for nonlinear systems with disturbance inputs based on logarithmic norms and quadratic programming. IEEE Trans Autom Control. 2019;65(7):3207–14.

    Article  MathSciNet  MATH  Google Scholar 

  20. Afanasiev VN, Kolmanovskii V, Nosov VR. Mathematical theory of control systems design, vol. 341. Dordrecht: Springer; 2013.

    Google Scholar 

  21. Li N, Geng S, Kolmanovsky IV, Girard A. An explicit reference governor for linear sampled-data systems with disturbance inputs and uncertain time delays. IEEE Trans Autom Control. 2022.

  22. Gilbert E. Functional expansions for the response of nonlinear differential systems. IEEE Trans Autom Control. 1977;22(6):909–21.

    Article  MathSciNet  MATH  Google Scholar 

  23. McDonough K, Kolmanovsky I. Controller state and reference governors for discrete-time linear systems with pointwise-in-time state and control constraints. In: 2015 American control conference (ACC). IEEE. 2015. pp. 3607–12.

  24. Kalabić UV, Li NI, Vermillion C, Kolmanovsky IV. Reference governors for chance-constrained systems. Automatica. 2019;109:108500.

  25. Li N, Girard A, Kolmanovsky I. Chance-constrained controller state and reference governor. Automatica. 2021;133:109864.

  26. Higgins MR, Burlion L. Implementation of a learning-based explicit reference governor for constrained control of a UAV. In: AIAA SCITECH 2022 forum. 2022. p. 2042.

  27. Liu K, Li N, Kolmanovsky I, Rizzo D, Girard A. Model-free learning for safety-critical control systems: a reference governor approach. In: 2020 American control conference (ACC). IEEE. 2020. pp. 943–9.

  28. Liu K, Li N, Kolmanovsky I, Rizzo D, Girard A. Safe learning reference governor: theory and application to fuel truck rollover avoidance. J Auton Veh Syst. 2021;1–19.

  29. Ikeya K, Liu K, Girard A, Kolmanovsky IV. Learning to satisfy constraints in spacecraft rendezvous and proximity maneuvering: a learning reference governor approach. In: AIAA SCITECH 2022 forum. 2022. p. 2514.

  30. Maldonado BP, Li N, Kolmanovsky I, Stefanopoulou AG. Learning reference governor for cycle-to-cycle combustion control with misfire avoidance in spark-ignition engines at high exhaust gas recirculation-diluted conditions. Int J Engine Res. 2020;21(10):1819–34.

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge their many collaborators, who are coauthors on joint publications, and, in particular, contributions of Dr. Uroš Kalabić to topics discussed which include the approach of extending the reduced order reference governor with flexible error budgeting.

Funding

This research is supported by the National Science Foundation grant number CMMI-1904394 and by the Air Force Office of Scientific Research under grant FA9550-20-1-0385.

Author information

Authors and Affiliations

  1. Department of Aerospace Engineering, The University of Michigan, 1320 Beal Avenue, Ann Arbor, MI, 48109-2140, USA

    Ilya Kolmanovsky

  2. Department of Aerospace Engineering, Auburn University, 211 Aerospace Engineering Bldg, AL, 36849, Auburn, USA

    Nan Li

Authors
  1. Ilya Kolmanovsky
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Nan Li
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ilya Kolmanovsky.

Ethics declarations

Conflict of Interest

The authors have declared that no conflict of interest exists.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Informatics in Control, Automation and Robotics” guest edited by Kurosh Madani, Oleg Gusikhin and Henk Nijmeijer.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kolmanovsky, I., Li, N. Protecting Systems from Violating Constraints Using Reference Governors. SN COMPUT. SCI. 3, 478 (2022). https://doi.org/10.1007/s42979-022-01374-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-022-01374-9

Keywords