Skip to main content
Log in

PID control of uncertain nonlinear stochastic systems with state observer

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

The classical proportional-integral-derivative (PID) controller is ubiquitous in engineering systems that are typically nonlinear with various uncertainties, including random noise. However, most of the literature on PID control focused on linear deterministic systems. Thus, a theory that explains the rationale of the linear PID when dealing with nonlinear uncertain stochastic systems and a concrete design method that can provide explicit formulas for PID parameters are required. Recently, we have demonstrated that the PID controller can globally stabilize a class of second-order nonlinear uncertain stochastic systems, where the derivative of the system output is assumed to be obtainable, which is generally unrealistic in practical applications. This has motivated us to present some theoretical results on PID control with a state observer for nonlinear uncertain stochastic systems. Specifically, a five-dimensional parameter manifold can be explicitly constructed, within which the three PID parameters and two observer gain parameters can be arbitrarily selected to globally stabilize nonlinear uncertain stochastic systems, as long as some knowledge about the unknown nonlinear drift and diffusion terms is available.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Åström K J, Hägglund T. PID Controllers: Theory, Design and Tuning. Alexander County: Instrument Society of America, 1995

    Google Scholar 

  2. Åström K J, Hägglund T. Advanced PID Control. Research Triangle Park: International Society of Automation, 2006

    Google Scholar 

  3. Ho M T, Lin C Y. PID controller design for robust performance. IEEE Trans Autom Control, 2003, 48: 1404–1409

    Article  MathSciNet  Google Scholar 

  4. Söylemez M T, Munro N, Baki H. Fast calculation of stabilizing PID controllers. Automatica, 2003, 39: 121–126

    Article  MathSciNet  Google Scholar 

  5. Silva G J, Datta A, Bhattacharyya S P. PID Controllers for Time-Delay Systems. Boston: Birkhäuser, 2004

    MATH  Google Scholar 

  6. Killingsworth N J, Krstic M. PID tuning using extremum seeking: online, model-free performance optimization. IEEE Control Syst, 2006, 26: 70–79

    Article  MathSciNet  Google Scholar 

  7. Keel L H, Bhattacharyya S P. Controller synthesis free of analytical models: three term controllers. IEEE Trans Autom Control, 2008, 53: 1353–1369

    Article  MathSciNet  Google Scholar 

  8. Qiao D W, Mu N K, Liao X F, et al. Improved evolutionary algorithm and its application in PID controller optimization. Sci China Inf Sci, 2020, 63: 199205

    Article  Google Scholar 

  9. Guo L. Some perspectives on the development of control theory (in Chinese). J Syst Sci Math Sci, 2011, 31: 1014–1018

    Google Scholar 

  10. Guo L. Feedback and uncertainty: some basic problems and results. Annu Rev Control, 2020, 49: 27–36

    Article  MathSciNet  Google Scholar 

  11. Zhao C, Guo L. On the capability of PID control for nonlinear uncertain systems. IFAC-PapersOnLine, 2017, 50: 1521–1526

    Article  Google Scholar 

  12. Zhao C, Guo L. PID controller design for second order nonlinear uncertain systems. Sci China Inf Sci, 2017, 60: 022201

    Article  MathSciNet  Google Scholar 

  13. Cong X R, Guo L. PID control for a class of nonlinear uncertain stochastic systems. In: Proceedings of the 56th Annual Conference on Decision and Control, Melbourne, 2017

  14. Koralov L B, Sinai Y G. Theory of Probability and Random Processes. Berlin: Springer, 2007

    Book  Google Scholar 

  15. Khasminskii R. Stochastic Stability of Differential Equations. Berlin: Springer, 2012

    Book  Google Scholar 

  16. Mao X R. Stochastic Differential Equations and Applications. Chichester: Horwood Publishing, 2008

    Book  Google Scholar 

  17. Duan J Q. An Introduction to Stochastic Dynamics. Beijing: Science Press, 2015

    MATH  Google Scholar 

  18. Guo Y C. Stochastic Processes and Control Theory. Beijing: Tsinghua University Press, 2017

    Google Scholar 

  19. Zhang T L, Deng F Q, Zhang W H. Study on stability in probability of general discrete-time stochastic systems. Sci China Inf Sci, 2020, 63: 159205

    Article  MathSciNet  Google Scholar 

  20. Reissig R, Sansone G, Conti R. Non-linear Differential Equations of Higher Order. Berlin: Springer, 1974

    MATH  Google Scholar 

  21. Zhong S, Huang Y, Guo L. A parameter formula connecting PID and ADRC. Sci China Inf Sci, 2020, 63: 192203

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the Youth Scholars Fund of Beijing Technology and Business University (Grant No. PXM2018_014213_000033) and National Natural Science Foundation of China (Grant No. 61973329). The authors would like to thank Professor Lei GUO from Academy of Mathematics and Systems Science, Chinese Academy of Sciences, for valuable discussion on PID control of nonlinear stochastic systems.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cheng Zhao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cong, X., Zhao, C. PID control of uncertain nonlinear stochastic systems with state observer. Sci. China Inf. Sci. 64, 192201 (2021). https://doi.org/10.1007/s11432-020-2979-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-2979-0

Keywords

Navigation