Skip to main content

Advertisement

Log in

Input-and-measurement event-triggered control for flexible air-breathing hypersonic vehicles with asymmetric partial-state constraints

  • Original paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

In this paper, an input-and-measurement event-triggered control scheme considering asymmetric partial-state constraints is proposed for flexible air-breathing hypersonic vehicles (FAHV) subject to lumped disturbances and limited resources. To realize a precise disturbance rejection with decreased communication burden in sensor-to-control channels, intermittent measurement-based extended state observers using switching threshold samplers are developed in altitude and velocity subsystems, while the quantitative relationship between the upper bounds of observation errors and the design parameters of switching triggering mechanism is derived. Subsequently, to ensure the angle of attack (AoA) well within the allowable operational region and simultaneously achieve a reference tracking with expected characteristic, asymmetric constraints imposed on partial states including AoA, velocity, and altitude are embedded in design process, while a one-to-one nonlinear mapping is designed to avoid the violation of state constraint of AoA without enforcing feasibility conditions on virtual control laws, and a modified prescribed performance control is constructed to govern the output constraints of velocity and altitude, releasing the demand on the precise knowledge of initial states. Next, to maintain the resources occupation (energy and communication in controller-to-actuator channel) at low levels and ensure a desirable tracking precision, robust control laws based on switching event-triggered mechanisms are designed for FAHV to circumvent Zeno phenomena and compensate for the sampling error induced by event-triggered conditions. The simulation results and comparisons validate the effectiveness of the proposed scheme.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. An, H., Guo, Z., Wang, G., Wang, C.: Low-complexity hypersonic flight control with asymmetric angle of attack constraint. Nonlinear Dyn. 100, 435–449 (2020)

    Article  Google Scholar 

  2. An, H., Wu, Q.: Switched-model-based compound control of hypersonic vehicles with input nonlinearities. Nonlinear Dyn. 98, 463–476 (2019)

    Article  Google Scholar 

  3. Bu, X., Lei, H.: A fuzzy wavelet neural network-based approach to hypersonic flight vehicle direct nonaffine hybrid control. Nonlinear Dyn. 94, 1657–1668 (2018)

    Article  Google Scholar 

  4. An, H., Xia, H., Wang, C.: Barrier Lyapunov function-based adaptive control for hypersonic flight vehicles. Nonlinear Dyn. 88(3), 1833–1853 (2017)

    Article  MathSciNet  Google Scholar 

  5. Bu, X.: Air-breathing hypersonic vehicles funnel control using neural approximation of non-affine dynamics. IEEE-ASME Trans. Mech. 23(5), 2099–2108 (2018)

    Article  Google Scholar 

  6. Zhang, Y., Li, R., Xue, T., Lei, Z.: Exponential sliding mode tracking control via back-stepping approach for a hypersonic vehicle with mismatched uncertainty. J. Franklin Inst. Eng. Appl. Math. 353, 2319–2343 (2016)

    Article  MathSciNet  Google Scholar 

  7. An, H., Liu, J., Wang, C.: Approximate back-stepping fault-tolerant control of the flexible air-breathing hypersonic Vehicle. IEEE Trans. Ind. Electron. 21(3), 1680–1691 (2016)

    Google Scholar 

  8. An, H., Wu, Q., Xia, H.: Control of a time-varying hypersonic vehicle model subject to inlet un-start condition. J. Franklin Inst. Eng. Appl. Math. 355, 4164–4197 (2019)

    Article  Google Scholar 

  9. Shi, Y., Shao, X., Zhang, W.: Quantized learning control for flexible air-breathing hypersonic vehicle with limited actuator bandwidth and prescribed performance. Aerosp. Sci. Technol. 97, 105629 (2020)

    Article  Google Scholar 

  10. Bu, X., Xiao, Y., Lei, H.: An adaptive critic design-based fuzzy neural controller for hypersonic vehicles: predefined behavioral nonaffine control. IEEE/ASME Trans. Mech. 24(4), 1871–1881 (2019)

    Article  Google Scholar 

  11. Shen, Q., Jiang, B., Cocquempot, V.: Fault-tolerant control for T–S fuzzy systems with application to near-space hypersonic vehicle with actuator faults. IEEE Trans. Fuzzy Syst. 24(4), 652–665 (2012)

    Article  Google Scholar 

  12. Bechlioulis, C., Rovithakis, G.: A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems. Automatica 50(4), 1217–1226 (2014)

    Article  MathSciNet  Google Scholar 

  13. Wang, M., Yang, A.: Dynamic learning from adaptive neural control of robot manipulators with prescribed performance. IEEE Trans. Syst. Man Cybern. Syst. 47(8), 2244–2255 (2017)

    Article  MathSciNet  Google Scholar 

  14. Bu, X.: A prescribed performance control approach guaranteeing small overshoot for air-breathing hypersonic vehicles via neural approximation. Aerosp. Sci. Technol. 71, 485–498 (2017)

    Article  Google Scholar 

  15. Zhang, L., Yang, G.: Adaptive fuzzy prescribed performance control of nonlinear systems with hysteretic actuator nonlinearity and faults. IEEE Trans. Syst. Man Cybern. Syst. 18(12), 2349–2358 (2018)

    Article  Google Scholar 

  16. Shao, X., Si, H., Zhang, W.: Fuzzy wavelet neural control with improved prescribed performance for MEMS gyroscope subject to input quantization. Fuzzy Sets Syst. (2020). https://doi.org/10.1016/j.fss.2020.08.005

    Article  Google Scholar 

  17. Xu, B., Shi, Z., Sun, F., He, W.: Barrier Lyapunov function-based learning control of hypersonic flight vehicle with AOA constraint and actuator faults. IEEE Trans. Cybern. 49(3), 1147–1157 (2018)

    Google Scholar 

  18. Zhao, K., Song, Y.: Removing the feasibility conditions imposed on tracking control designs for state-constrained strict-feedback systems. IEEE Trans. Autom. Control 64(3), 1265–1272 (2019)

    Article  MathSciNet  Google Scholar 

  19. Shao, X., Shi, Y.: Neural adaptive control for MEMS gyroscope with full-state constraints and quantized input. IEEE Trans. Ind. Inform. 16(10), 6444–6454 (2020)

    Google Scholar 

  20. Xia, X., Zhang, T.: Robust adaptive quantized DSC of uncertain pure-feedback nonlinear systems with time-varying output and state constraints. Int. J. Robust Nonlinear Control 28(10), 3357–3375 (2018)

    Article  MathSciNet  Google Scholar 

  21. Hua, Y., Zhang, T.: Adaptive control of pure-feedback nonlinear systems with full-state time-varying constraints and unmodeled dynamics. Int. J. Adapt. Control Signal Process. 34(2), 183–198 (2019)

    Article  MathSciNet  Google Scholar 

  22. Xia, X., Zhang, T., Fang, Y., Kang, G.: Adaptive quantized control of output feedback nonlinear systems with input unmodeled dynamics based on backstepping and small-gain method. IEEE Trans. Syst. Man Cybern. Syst. (2019). https://doi.org/10.1109/tsmc.2019.2956997

    Article  Google Scholar 

  23. Wang, J., Liu, Z., Chen, C.: Event-triggered neural adaptive failure compensation control for stochastic systems with dead-zone output. Nonlinear Dyn. 96, 2179–2196 (2019)

    Article  Google Scholar 

  24. Yuan, Y., Zhang, P., Wang, Z.: Noncooperative event-triggered control strategy design with round-robin protocol: applications to load frequency control of circuit systems. IEEE Trans. Ind. Electron. 67(3), 2155–2166 (2020)

    Article  Google Scholar 

  25. Huang, Y., Wang, J., Shi, D., Wu, J.: Event-triggered sampled-data control: an active disturbance rejection approach. IEEE/ASME Trans. Mech. 24(5), 2052–2063 (2019)

    Article  Google Scholar 

  26. Xing, L., Wen, C., Liu, Z.: Event-triggered adaptive control for a class of uncertain nonlinear systems. IEEE Trans. Autom. Control 62(4), 2071–2076 (2017)

    Article  MathSciNet  Google Scholar 

  27. Choi, Y., Yoo, S.: Event-triggered output-feedback tracking of a class of nonlinear systems with unknown time delays. Nonlinear Dyn. 96, 959–973 (2019)

    Article  Google Scholar 

  28. Tabuada, P.: Event-triggered real-time scheduling of stabilizing control tasks. IEEE Trans. Autom. Control 52(9), 1680–1685 (2007)

    Article  MathSciNet  Google Scholar 

  29. Bu, X., Wu, X., Zhang, R.: Tracking differentiator design for the robust backstepping control of a flexible air-breathing hypersonic vehicle. J. Franklin Inst. Eng. Appl. Math. 352(4), 1739–1765 (2015)

    Article  MathSciNet  Google Scholar 

  30. Bolender, M., Doman, D.: Nonlinear longitudinal dynamical model of an air-breathing hypersonic vehicle. J. Spacecraft Rock. 44(2), 374–387 (2007)

    Article  Google Scholar 

  31. Wang, Y., Hu, J., Li, J.: Improved prescribed performance control for nonaffine pure-feedback systems with input saturation. Int. J. Robust Nonlinear Control 29(6), 1769–1788 (2019)

    Article  MathSciNet  Google Scholar 

  32. Bu, X., Wu, X., Zhu, F.: Novel prescribed performance neural control of a flexible air-breathing hypersonic vehicle with unknown initial errors. ISA Trans. 59, 149–159 (2015)

    Article  Google Scholar 

  33. Wu, Y., Jiang, B., Lu, N.: A descriptor system approach for estimation of incipient faults with application to high-speed railway traction devices. IEEE Trans. Syst. Man Cybern. Syst. 49(10), 2108–2118 (2019)

    Article  Google Scholar 

  34. Wu, Y., Jiang, B., Wand, Y.: Incipient winding fault detection and diagnosis for squirrel-cage induction motors equipped on CRH trains. ISA Trans. 99, 488–495 (2019)

    Article  Google Scholar 

  35. Guo, B., Zhao, Z.: On convergence of non-linear extended state observer for multi-input multi-output systems with uncertainty. IET Control Theory Appl. 6(15), 2375–2386 (2012)

    Article  MathSciNet  Google Scholar 

  36. Yuan, H., Wang, J., Shi, D.: Toward event-triggered extended state observer. IEEE Trans. Autom. Control 63(6), 1842–1849 (2018)

    Article  MathSciNet  Google Scholar 

  37. Shao, X., Wang, H.: Back-stepping robust trajectory linearization control for hypersonic reentry vehicle via novel tracking differentiator. J. Franklin Inst. Eng. Appl. Math. 353(9), 1957–1984 (2016)

    Article  MathSciNet  Google Scholar 

  38. Wang, X., Chen, Z., Yuan, Z.: Nonlinear tracking-differentiator with high speed in whole course. Control Theory Appl. 20(6), 875–878 (2003)

    Google Scholar 

  39. Shi, Y., Shao, X., Zhang, W.: Neural observer-based quantized output feedback control for MEMS gyroscopes with guaranteed transient performance. Aerosp. Sci. Technol. (2020). https://doi.org/10.1016/j.ast.2020.106055

    Article  Google Scholar 

  40. Shao, X., Liu, J., Cao, H.: Robust dynamic surface trajectory tracking control for a quadrotor UAV via extended state observer. Int. J. Robust Nonlinear Control 28(7), 2700–2719 (2018)

    Article  MathSciNet  Google Scholar 

  41. Shao, X., Wang, L., Li, J.: High-order ESO based output feedback dynamic surface control for quadrotors under position constraints and uncertainties. Aerosp. Sci. Technol. 89, 288–298 (2019)

    Article  Google Scholar 

  42. Liu, N., Shao, X.: Desired compensation RISE-based IBVS control of quadrotors for tracking a moving target. Nonlinear Dyn. 95, 2605–2624 (2019)

    Article  Google Scholar 

  43. Liu, S., Liu, Y., Liang, X.: Uncertainty observation-based adaptive succinct fuzzy-neuro dynamic surface control for trajectory tracking of fully actuated underwater vehicle system with input saturation. Nonlinear Dyn. 98(8), 1683–1699 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

This research has been supported in part by National Natural Science Foundation of China under grant 61803348, State Key Laboratory of Deep Buried Target Damage under grant DXMBJJ2019-02, Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi under grant 2020L0266, Shanxi Province Science Foundation for Youths under grant 201701D221123, Youth Academic Leader Program of North University of China under grant QX201803, Program for the Innovative Talents of Higher Education Institutions of Shanxi, and Shanxi “1331 Project” Key Subjects Construction (1331KSC).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xingling Shao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shao, X., Shi, Y. & Zhang, W. Input-and-measurement event-triggered control for flexible air-breathing hypersonic vehicles with asymmetric partial-state constraints. Nonlinear Dyn 102, 163–183 (2020). https://doi.org/10.1007/s11071-020-05942-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-020-05942-7

Keywords

Navigation