Nonlinear Dynamics

, Volume 95, Issue 2, pp 1009–1025 | Cite as

Coupling effect-triggered control strategy for hypersonic flight vehicles with finite-time convergence

  • Zongyi GuoEmail author
  • Jianguo Guo
  • Jing Chang
  • Jun Zhou
Original Paper


This paper investigates a novel control strategy of addressing coupling issue for attitude tracking control of hypersonic flight vehicle. By using a defined coupling effect indicator which demonstrates whether a coupling harms or benefits the system, one finite-time coupling effect-triggered control scheme is proposed via applying the second-order sliding mode control technique. A specific nonlinear function is designed to implement the addition of beneficial couplings or removal of detrimental couplings. As a consequence, the proposed method establishes a unified couplings recognition and coupling effect-triggered control framework, which leads to a potential of improving the dynamic performance. The chattering attenuation is also involved for practical implementation. Finally, simulation results illustrate the validity of the proposed control scheme.


Coupling effect Sliding mode Attitude control Hypersonic flight vehicles 



The authors would like to greatly appreciate the editor and all the anonymous reviewers for their comments, which helped to improve the quality of this paper. This work was supported by National Natural Science Foundation of China under Grants 61803308 and 61703339.

Compliance with ethical standards

Conflict of interest

No potential conflict of interest was reported by the authors.


  1. 1.
    Parker, J., Serrani, A., Yurkovich, S., Doman, D.: Control-oriented modeling of an air-breathing hypersonic vehicle. J. Guid. Control Dyn. 30(3), 856–869 (2007)CrossRefGoogle Scholar
  2. 2.
    Sziroczak, D., Smith, H.: A review of design issues specific to hypersonic flight vehicles. Prog. Aerosp. Sci. 84, 1–28 (2016)CrossRefGoogle Scholar
  3. 3.
    Mu, C., Ni, Z., Sun, C., He, H.: Air-breathing hypersonic vehicle tracking control based on adaptive dynamic programming. IEEE Trans. Neural Netw. Learn. Syst. 28(3), 584–598 (2017)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Sagliano, M., Mooij, E., Theil, S.: Adaptive disturbance-based high-order sliding-mode control for hypersonic-entry vehicles. J. Guid. Control Dyn. 40(3), 521–535 (2017)CrossRefGoogle Scholar
  5. 5.
    Basin, M., Yu, P., Shtessel, Y.: Hypersonic missile adaptive sliding mode control using finite- and fixed-time observers. IEEE Trans. Ind. Electron. 65(1), 930–941 (2018)CrossRefGoogle Scholar
  6. 6.
    Guo, Z., Zhou, J., Guo, J., Cieslak, J., Chang, J.: Coupling characterization-based robust attitude control scheme for hypersonic vehicles. IEEE Trans. Ind. Electron. 64(8), 6350–6361 (2017)CrossRefGoogle Scholar
  7. 7.
    An, H., Xia, H., Wang, C.: Barrier Lyapunov function-based adaptive control for hypersonic flight vehicles. Nonlinear Dyn. 88(3), 1833–1853 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Bu, X.: Guaranteeing prescribed output tracking performance for air-breathing hypersonic vehicles via non-affine back-stepping control design. Nonlinear Dyn. 91(1), 525–538 (2018)CrossRefzbMATHGoogle Scholar
  9. 9.
    Snell, A.: Decoupling control design with applications to flight. J. Guid. Control. Dyn. 21(4), 647–655 (1998)CrossRefGoogle Scholar
  10. 10.
    Georgie, J., Valasek, J.: Evaluation of longitudinal desired dynamics for dynamic inversion controlled generic reentry vehicles. J. Guid. Control. Dyn. 26(5), 811–819 (2003)CrossRefGoogle Scholar
  11. 11.
    Su, X., Jia, Y.: Self-scheduled robust decoupling control with \(H_\infty \) performance of hypersonic vehicles. Syst. Control Lett. 70, 38–48 (2014)CrossRefzbMATHGoogle Scholar
  12. 12.
    Chen, W., Yang, J., Guo, L., Li, S.: Disturbance-observer-based control and related methods—an overview. IEEE Trans. Ind. Electron. 63(2), 1083–1095 (2016)CrossRefGoogle Scholar
  13. 13.
    Yao, X., Guo, L.: Composite anti-disturbance control for Markovian jump nonlinear systems via disturbance observer. Automatica 49(8), 2538–2545 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Yao, X., Guo, L., Wu, L., Dong, H.: Static anti-windup design for nonlinear Markovian jump systems with multiple disturbances. Inf. Sci. 418–419, 169–183 (2017)CrossRefGoogle Scholar
  15. 15.
    Edwards, C., Spurgeon, S.: Sliding Mode Control: Theory and Applications. Talyor & Francis, London (1998)CrossRefzbMATHGoogle Scholar
  16. 16.
    Raffo, G., Ortega, M., Rubio, F.: An integral predictive/ nonlinear \(H_\infty \) control structure for a quadrotor helicopter. Automatica 461, 29–39 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Duan, Z., Huang, L., Yang, J., Qin, G.: On decoupled or coupled control of bank-to-turn missiles. Sci. China Inf. Sci. 58, 1–13 (2015)CrossRefGoogle Scholar
  18. 18.
    Bristol, E.: On a new measure of interaction for multivariable process control. IEEE Trans. Autom. Control AC11, 133–134 (1966)CrossRefGoogle Scholar
  19. 19.
    Salgado, M., Conley, A.: MIMO interaction measure and controller structure selection. Int. J. Control 77, 367–383 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    van de Wal, M., de Jager, B.: A review of methods for input/output selection. Automatica 37(4), 487–510 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Castano, M., Birk, W., Nikolakopoulos, G.: A survey on control configuration selection and new challenges in relation to wireless sensor and actuator networks. In: IFAC World Congress, July 9–12, pp. 8810–8825 (2017)Google Scholar
  22. 22.
    Goodwin, G., Salgado, M., Silva, E.: Time-domain performance limitations arising from decentralized architectures and their relationship to the RGA. Int. J. Control 78(13), 1045–1062 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Gigi, S., Tangirala, A.: Quantification of interaction in multiloop control system using directed spectral decomposition. Automatica 49(5), 1174–1783 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Shtessel, Y., Edwards, C., Fridman, L., Levant, A.: Sliding Mode Control and Observation. Springer, New York (2013). Chaps. 4Google Scholar
  25. 25.
    Guo, Z., Chang, J., Guo, J., Zhou, J.: Adaptive twisting sliding mode algorithm for hypersonic reentry vehicle attitude control based on finite-time observer. ISA Trans. 77, 20–29 (2018)CrossRefGoogle Scholar
  26. 26.
    Chalanga, A., Kamal, S., Fridman, L., Bandyopadhyay, B., Moreno, J.: Implementation of super-twisting control super-twisting and higher order sliding mode observer based approaches. IEEE Trans. Ind. Electron. 63(6), 3677–3685 (2016)CrossRefGoogle Scholar
  27. 27.
    Chang, J., Cieslak, J., Dvila, J., Zolghadri, A., Zhou, J.: Analysis and design of second-order sliding-mode algorithms for quadrotor roll and pitch estimation. ISA Trans. 71(Pt 2), 495–512 (2017)CrossRefGoogle Scholar
  28. 28.
    Yao, X., Park, J., Dong, H., Guo, L., Lin, X.: Robust adaptive nonsingular terminal sliding mode control for automatic train. IEEE Trans. Syst. Man Cybern. Syst.
  29. 29.
    Tian, B., Yin, L., Wang, H.: Finite-time reentry attitude control based on adaptive multivariable disturbance compensation. IEEE Trans. Ind. Electron. 62(9), 5889–5898 (2015)CrossRefGoogle Scholar
  30. 30.
    Shao, X., Wang, H.: Sliding mode based trajectory linearization control for hypersonic reentry vehicle via extended disturbance observer. ISA Trans. 53, 1771–1786 (2014)CrossRefGoogle Scholar
  31. 31.
    Dong, W., Farrell, J., Polycarpou, M., Djapic, V., Sharma, M.: Command filtered adaptive backstepping. IEEE Trans. Control Syst. Technol. 20(3), 566–580 (2012)CrossRefGoogle Scholar
  32. 32.
    Alwi, H., Edwards, C.: An adaptive sliding mode differentiator for actuator oscillatory failure case reconstruction. Automatica 49, 642–651 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Shinner, S.: Modern Control System Theory and Application, 2nd edn. Addison-Wesley, Reading (1978)Google Scholar
  34. 34.
    Liu, H., Xi, J., Zhong, Y.: Robust attitude stabilization for nonlinear quadrotor systems with uncertainties and delays. IEEE Trans. Ind. Electron. 64(7), 5585–5594 (2017)CrossRefGoogle Scholar
  35. 35.
    Liu, H., Zhao, W., Zuo, Z., Zhong, Y.: Robust control for quadrotors with multiple time-varying uncertainties and delays. IEEE Trans. Ind. Electron. 64(2), 1303–1312 (2017)CrossRefGoogle Scholar
  36. 36.
    Tabuada, P.: Event-triggered real-time scheduling of stabilizing control tasks. IEEE Trans. Autom. Control 52(9), 1680–1685 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Su, X., Liu, Z., Lai, G.: Event-triggered robust adaptive control for uncertain nonlinear systems preceded by actuator dead-zone. Nonlinear Dyn. 93(2), 219–231 (2018)CrossRefzbMATHGoogle Scholar
  38. 38.
    Zhang, X., Han, Q., Zhang, B.: An overview and deep investigation on sampled-data-based event-triggered control and filtering for networked systems. IEEE Trans. Ind. Inform. 13(1), 4–16 (2017)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Institute of Precision Guidance and ControlNorthwestern Polytechnical UniversityXi’anChina
  2. 2.School of Aerospace Science and TechnologyXidian UniversityXi’anChina

Personalised recommendations