Skip to main content
Log in

Finite-time command filtered adaptive control for nonlinear systems via immersion and invariance

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

Abstract

This paper investigates the problem of finite-time adaptive output tracking control for strict-feedback nonlinear systems with parametric uncertainties. Command signals and their derivatives are generated by a new command filter based on a second-order finite-time differentiator, which attenuates the chattering phenomenon. The parameter estimations are achieved by an immersion and invariance approach without requiring the certainty equivalence principle. The finite-time adaptive controller is constructed via a backstepping design method, a finite-time command filter, and a modified fractional-order error compensation mechanism. The proposed control strategy guarantees the finite-time boundedness of all signals in the closed-loop system, and the tracking error is driven into an arbitrarily small neighborhood of the origin in finite time. Finally, the new design technique is validated in a simulation example of the electromechanical system.

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.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kanellakopoulos I, Kokotovic P V, Morse A S. Systematic design of adaptive controllers for feedback linearizable systems. IEEE Trans Autom Control, 1991, 36: 1241–1253

    Article  MathSciNet  Google Scholar 

  2. Jiang Z P, Praly L. Design of robust adaptive controllers for nonlinear systems with dynamic uncertainties. Automatica, 1998, 34: 825–840

    Article  MathSciNet  Google Scholar 

  3. Ge S S, Hong F, Lee T H. Robust adaptive control of nonlinear systems with unknown time delays. Automatica, 2005, 41: 1181–1190

    Article  MathSciNet  Google Scholar 

  4. Tong S C, Li Y M. Observer-based adaptive fuzzy backstepping control of uncertain nonlinear pure-feedback systems. Sci China Inf Sci, 2014, 57: 012204

    Article  MathSciNet  Google Scholar 

  5. Krstic M, Kanellakopoulos I, Kokotovic P V. Nonlinear and Adaptive Control Design. New York: Wiley, 1995

    MATH  Google Scholar 

  6. Zhou J, Wen C Y. Adaptive Backstepping Control of Uncertain Systems: Nonsmooth Nonlinearities, Interactions or Time-Variations. Berlin: Springer, 2008

    MATH  Google Scholar 

  7. Swaroop D, Hedrick J K, Yip P P, et al. Dynamic surface control for a class of nonlinear systems. IEEE Trans Autom Control, 2000, 45: 1893–1899

    Article  MathSciNet  Google Scholar 

  8. Wang D, Huang J. Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Trans Neural Netw, 2005, 16: 195–202

    Article  Google Scholar 

  9. Yoo S J, Park J B, Choi Y H. Adaptive dynamic surface control for stabilization of parametric strict-feedback nonlinear systems with unknown time delays. IEEE Trans Autom Control, 2007, 52: 2360–2365

    Article  MathSciNet  Google Scholar 

  10. Ma H, Liang H J, Zhou Q, et al. Adaptive dynamic surface control design for uncertain nonlinear strict-feedback systems with unknown control direction and disturbances. IEEE Trans Syst Man Cybern Syst, 2019, 49: 506–515

    Article  Google Scholar 

  11. Zhang G Q, Deng Y J, Zhang W D, et al. Robust neural output-feedback stabilization for stochastic nonlinear process with time-varying delay and unknown dead zone. Sci China Inf Sci, 2017, 60: 120202

    Article  MathSciNet  Google Scholar 

  12. Farrell J A, Polycarpou M, Sharma M, et al. Command filtered backstepping. IEEE Trans Autom Control, 2009, 54: 1391–1395

    Article  MathSciNet  Google Scholar 

  13. Dong W J, Farrell J A, Polycarpou M M, et al. Command filtered adaptive backstepping. IEEE Trans Contr Syst Technol, 2012, 20: 566–580

    Article  Google Scholar 

  14. Yu J P, Shi P, Dong W J, et al. Observer and command-filter-based adaptive fuzzy output feedback control of uncertain nonlinear systems. IEEE Trans Ind Electron, 2015, 62: 5962–5970

    Article  Google Scholar 

  15. Yu J P, Zhao L, Yu H S, et al. Barrier Lyapunov functions-based command filtered output feedback control for full-state constrained nonlinear systems. Automatica, 2019, 105: 71–79

    Article  MathSciNet  Google Scholar 

  16. Xia J W, Zhang J, Feng J, et al. Command filter-based adaptive fuzzy control for nonlinear systems with unknown control directions. IEEE Trans Syst Man Cybern Syst, 2021, 51: 1945–1953

    Google Scholar 

  17. Astolfi A, Ortega R. Immersion and invariance: a new tool for stabilization and adaptive control of nonlinear systems. IEEE Trans Autom Control, 2003, 48: 590–606

    Article  MathSciNet  Google Scholar 

  18. Astolfi A, Karagiannis D, Ortega R. Nonlinear and Adaptive Control with Applications. Berlin: Springer, 2007

    MATH  Google Scholar 

  19. Karagiannis D, Astolfi A. Nonlinear adaptive control of systems in feedback form: an alternative to adaptive backstepping. Syst Control Lett, 2008, 57: 733–739

    Article  MathSciNet  Google Scholar 

  20. Liu X B, Ortega R, Su H, et al. Immersion and invariance adaptive control of nonlinearly parameterized nonlinear systems. IEEE Trans Autom Control, 2010, 55: 2209–2214

    Article  MathSciNet  Google Scholar 

  21. Zhao B, Xian B, Zhang Y, et al. Nonlinear robust adaptive tracking control of a quadrotor UAV via immersion and invariance methodology. IEEE Trans Ind Electron, 2015, 62: 2891–2902

    Article  Google Scholar 

  22. Liu Z, Tan X M, Yuan R Y, et al. Immersion and invariance-based output feedback control of air-breathing hypersonic vehicles. IEEE Trans Autom Sci Eng, 2016, 13: 394–402

    Article  Google Scholar 

  23. Hu J C, Zhang H H. Immersion and invariance based command-filtered adaptive backstepping control of VTOL vehicles. Automatica, 2013, 49: 2160–2167

    Article  MathSciNet  Google Scholar 

  24. Huang X Q, Lin W, Yang B. Global finite-time stabilization of a class of uncertain nonlinear systems. Automatica, 2005, 41: 881–888

    Article  MathSciNet  Google Scholar 

  25. Min H F, Xu S Y, Gu J, et al. Adaptive finite-time control for high-order nonlinear systems with multiple uncertainties and its application. IEEE Trans Circ Syst I, 2020, 67: 1752–1761

    MathSciNet  MATH  Google Scholar 

  26. Liu Y, Liu X P, Jing Y W, et al. Direct adaptive preassigned finite-time control with time-delay and quantized input using neural network. IEEE Trans Neural Netw Learn Syst, 2020, 31: 1222–1231

    Article  MathSciNet  Google Scholar 

  27. Xia J W, Zhang J, Sun W, et al. Finite-time adaptive fuzzy control for nonlinear systems with full state constraints. IEEE Trans Syst Man Cybern Syst, 2019, 49: 1541–1548

    Article  Google Scholar 

  28. Chen X Y, Huang T W, Cao J D, et al. Finite-time multi-switching sliding mode synchronisation for multiple uncertain complex chaotic systems with network transmission mode. IET Control Theory Appl, 2019, 13: 1246–1257

    Article  MathSciNet  Google Scholar 

  29. Li Y M, Li K W, Tong S C. Finite-time adaptive fuzzy output feedback dynamic surface control for MIMO nonstrict feedback systems. IEEE Trans Fuzzy Syst, 2019, 27: 96–110

    Article  Google Scholar 

  30. Wang F, Chen B, Lin C, et al. Adaptive neural network finite-time output feedback control of quantized nonlinear systems. IEEE Trans Cybern, 2018, 48: 1839–1848

    Article  Google Scholar 

  31. Wang F, Chen B, Liu X P, et al. Finite-time adaptive fuzzy tracking control design for nonlinear systems. IEEE Trans Fuzzy Syst, 2018, 26: 1207–1216

    Article  Google Scholar 

  32. Liu Y, Liu X P, Jing Y W, et al. A novel finite-time adaptive fuzzy tracking control scheme for nonstrict feedback systems. IEEE Trans Fuzzy Syst, 2019, 27: 646–658

    Article  Google Scholar 

  33. Zhao L, Yu J P, Shi P. Command filtered backstepping-based attitude containment control for spacecraft formation. IEEE Trans Syst Man Cybern Syst, 2021, 51: 1278–1287

    Article  Google Scholar 

  34. Zhao L, Yu J P, Lin C, et al. Adaptive neural consensus tracking for nonlinear multiagent systems using finite-time command filtered backstepping. IEEE Trans Syst Man Cybern Syst, 2018, 48: 2003–2012

    Article  Google Scholar 

  35. Yu J P, Zhao L, Yu H S, et al. Fuzzy finite-time command filtered control of nonlinear systems with input saturation. IEEE Trans Cybern, 2018, 48: 2378–2387

    Article  Google Scholar 

  36. Yu J P, Zhao L, Shi P. Finite-time command filtered backstepping control for a class of nonlinear systems. Automatica, 2018, 92: 173–180

    Article  MathSciNet  Google Scholar 

  37. Qian C J, Lin W. A continuous feedback approach to global strong stabilization of nonlinear systems. IEEE Trans Autom Control, 2001, 46: 1061–1079

    Article  MathSciNet  Google Scholar 

  38. Wang X H, Chen Z Q, Yang G. Finite-time-convergent differentiator based on singular perturbation technique. IEEE Trans Autom Control, 2007, 52: 1731–1737

    Article  MathSciNet  Google Scholar 

  39. Dawson D M, Carroll J J, Schneider M. Integrator backstepping control of a brush DC motor turning a robotic load. IEEE Trans Control Syst Technol, 1994, 2: 233–244

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61973179, U1813201, 61973131, 61703059), Japan Society for the Promotion of Science (Grant No. C-18K04212), Taishan Scholar Special Project Fund (Grant No. TSQN20161026), and Natural Science Foundation of Jiangsu Province (Grant No. BK20170291).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jinpeng Yu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, J., Shi, P., Chen, X. et al. Finite-time command filtered adaptive control for nonlinear systems via immersion and invariance. Sci. China Inf. Sci. 64, 192202 (2021). https://doi.org/10.1007/s11432-020-3144-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-3144-6

Keywords

Navigation