Skip to main content
SpringerLink
Log in

Fuzzy Disturbance Observer Design for a Class of Nonlinear SISO Systems

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

In this paper, a novel fuzzy disturbance observer (DOB) is designed for a class of nonlinear single-input–single-output (SISO) systems. The main feature of the proposed DOB lies in that only the control input and system output information is required. According to the output information and mathematical model of the original SISO system, an auxiliary system is constructed to estimate the disturbance. The estimation result is generated by comparing the control input of the original system with the input variable of the auxiliary system. On this basis, a fuzzy logic controller is introduced to obtain the time-varying parameter such that the estimation error can be automatically minimized. Then, the design method of the aforementioned fuzzy DOB is systematically described through a numerical example and the continuous stirred tank reactor system. Finally, a fuzzy DOB based composite controller is presented for the missile roll stabilization system to verify its effectiveness in practical engineering.

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

Access this article

Log in via an institution

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
Fig. 13

Similar content being viewed by others

References

  1. Saeedi, M., Moradi, M., Hosseini, M., Emamifar, A., Ghadimi, N.: Robust optimization based optimal chiller loading under cooling demand uncertainty. Appl. Therm. Eng. 148, 1081–1091 (2019)

    Article  Google Scholar 

  2. Liu, L., Ding, S.H.: A unified control approach to finite-time stabilization of SOSM dynamics subject to an output constraint. Appl. Math. Comput. 394, Art. no. 125752 (2021)

  3. Yuan, J.H., Ding, S.H., Mei, K.Q.: Fixed-time SOSM controller design with output constraint. Nonlinear Dyn. 102(3), 1567–1583 (2020)

    Article  Google Scholar 

  4. Fang, Y.M., Fei, J.T., Cao, D.: Adaptive fuzzy-neural fractional-order current control of active power filter with finite-time sliding controller. Int. J. Fuzzy Syst. 21(5), 1533–1543 (2019)

    Article  MathSciNet  Google Scholar 

  5. Ding, S.H., Liu, L., Zheng, W.X.: Sliding mode direct yaw-moment control design for in-wheel electric vehicles. IEEE Trans. Ind. Electron. 64(8), 6752–6762 (2017)

    Article  Google Scholar 

  6. Mei, K.Q., Ding, S.H.: Second-order sliding mode controller design subject to an upper-triangular structure. IEEE Trans. Syst. Man Cybern. Syst. 51(1), 497–507 (2021)

    Article  Google Scholar 

  7. Wen, S.P., Chen, M., Zeng, Z.G., Huang, T.W., Li, C.J.: Adaptive neural-fuzzy sliding-mode fault-tolerant control for uncertain nonlinear systems. IEEE Trans. Syst. Man Cybern. Syst. 47(8), 2268–2278 (2017)

    Article  Google Scholar 

  8. Shi, C.X., Yang, G.H., Li, X.J.: Robust adaptive backstepping control for hierarchical multi-agent systems with signed weights and system uncertainties. IET Control Theory Appl. 11(16), 2743–2752 (2017)

    Article  MathSciNet  Google Scholar 

  9. Luo, S., Lewis, F.L., Song, Y.D., Vamvoudakis, K.G.: Adaptive backstepping optimal control of a fractional-order chaotic magnetic-field electromechanical transducer. Nonlinear Dyn. 100(1), 523–540 (2020)

    Article  Google Scholar 

  10. Hou, Q.K., Ding, S.H., Yu, X.Y.: Composite super-twisting sliding mode control design for PMSM speed regulation problem based on a novel disturbance observer. IEEE Trans. Energy Convers. (2020). https://doi.org/10.1109/TEC.2020.2985054

    Article  Google Scholar 

  11. Wang, N., Sun, Z., Su, S.F., Wang, Y.Y.: Fuzzy uncertainty observer-based path-following control of underactuated marine vehicles with unmodeled dynamics and disturbances. Int. J. Fuzzy Syst. 20(8), 2593–2604 (2018)

    Article  MathSciNet  Google Scholar 

  12. Chen, W.H.: Disturbance observer based control for nonlinear systems. IEEE–ASME Trans. Mechatron. 9(4), 706–710 (2004)

    Article  Google Scholar 

  13. Hou, Q.K., Ding, S.H.: GPIO based super-twisting sliding mode control for PMSM. IEEE Trans. Circuits Syst. II 68(2), 747–751 (2021)

    Article  Google Scholar 

  14. Mei, K.Q., Ma, L., He, R.X., Ding, S.H.: Finite-time controller design of multiple integrator nonlinear systems with input saturation. Appl. Math. Comput. (2019). https://doi.org/10.1016/j.amc.2019.124986

    Article  MATH  Google Scholar 

  15. Ohishi, K., Nakao, M., Ohishi, K., Miyachi, K.: Microprocessor-controlled DC motor for load-insensitive position servo system. IEEE Trans. Ind. Electron. 34(1), 44–49 (1987)

    Article  Google Scholar 

  16. Park, G., Kim, H.: Adaptive regulation to nominal response for uncertain mechanical systems and its application to optical disk drive. IEEE Trans. Ind. Electron. 65(2), 1450–1458 (2018)

    Article  Google Scholar 

  17. Wei, H., Ouyang, Y.C., Hong, J.: Vibration control of a flexible robotic manipulator in the presence of input deadzone. IEEE Trans. Ind. Inform. 13(1), 48–59 (2017)

    Article  Google Scholar 

  18. Li, Z.Y., Zhang, L.X., Liu, H., Zong, Z.Y., Liu, C.J.: Nonlinear robust control of tail-sitter aircrafts in flight mode transitions. Aerosp. Sci. Technol. 81, 348–361 (2018)

    Article  Google Scholar 

  19. Wang, L., Su, J.B.: Disturbance rejection control for non-minimum phase systems with optimal disturbance observer. ISA Trans. 57, 1–9 (2015)

    Article  Google Scholar 

  20. Yang, Z.J., Tsubakihara, H., Kanae, S., Wada, K., Su, C.Y.: A novel robust nonlinear motion controller with disturbance observer. IEEE Trans. Control Syst. Technol. 16(1), 137–147 (2008)

    Article  Google Scholar 

  21. Ma, L., Huo, X., Zhao, X.D., Zong, G.D.: Adaptive fuzzy tracking control for a class of uncertain switched nonlinear systems with multiple constraints: a small-gain approach. Int. J. Fuzzy Syst. 21(8), 2609–2624 (2019)

    Article  MathSciNet  Google Scholar 

  22. Sun, Z., Ling, Y.W., Qu, H.B., Xiang, F., Sun, Z.X., Wu, F.: An adaptive DE algorithm based fuzzy logic anti-swing controller for overhead crane systems. Int. J. Fuzzy Syst. 22(6), 1905–1921 (2020)

    Article  Google Scholar 

  23. Chen, W.H., Ballance, D.J., Gawthrop, P.J., OReilly, J.: A nonlinear disturbance observer for robotic manipulators. IEEE Trans. Ind. Electron. 47(4), 932–938 (2000)

    Article  Google Scholar 

  24. Guo, L., Chen, W.H.: Disturbance attenuation and rejection for systems with nonlinearity via DOBC approach. Int. J. Robust Nonlinear Control 15(3), 109–125 (2005)

    Article  MathSciNet  Google Scholar 

  25. Kim, E., Lee, S.: Output feedback tracking control of MIMO systems using a fuzzy disturbance observer and its application to the speed control of a PM synchronous motor. IEEE Trans. Fuzzy Syst. 13(6), 725–741 (2005)

    Article  Google Scholar 

  26. Chen, X.S., Li, J., Yang, J., Li, S.H.: A disturbance observer enhanced composite cascade control with experimental studies. Int. J. Control Autom. Syst. 11(3), 555–562 (2013)

    Article  Google Scholar 

  27. Ding, S.H., Chen, W.H., Mei, K.Q., Murray-Smith, D.J.: Disturbance observer design for nonlinear systems represented by input–output models. IEEE Trans. Ind. Electron. 67(2), 1222–1232 (2020)

    Article  Google Scholar 

  28. Wu, Y.Q., Yu, J.B., Zhang, Z.C.: Output feedback regulation control for a class of cascade nonlinear systems by time-varying Kalman observer. Int. J. Robust Nonlinear Control 29(7), 2149–2170 (2019)

    Article  MathSciNet  Google Scholar 

  29. Liu, J.X., Laghrouche, S., Harmouche, M., Wack, M.: Adaptive-gain second-order sliding mode observer design for switching power converters. Control Eng. Pract. 30, 124–131 (2014)

    Article  Google Scholar 

  30. Lu, S.S., Tian, C., Yan, P.: Adaptive extended state observer-based synergetic control for a long-stroke compliant microstage with stress stiffening. IEEE–ASME Trans. Mechatron. 25(1), 259–270 (2020)

    Article  Google Scholar 

  31. Abedinia, O., Zareinejad, M., Doranehgard, M.H., Fathi, G., Ghadimi, N.: Optimal offering and bidding strategies of renewable energy based large consumer using a novel hybrid robust-stochastic approach. J. Clean. Prod. 215, 878–889 (2019)

    Article  Google Scholar 

  32. Gao, W., Darvishan, A., Toghani, M., Mohammadi, M., Abedinia, O., Ghadimi, N.: Different states of multi-block based forecast engine for price and load prediction. Int. J. Electr. Power Energy Syst. 104, 423–435 (2019)

    Article  Google Scholar 

  33. Ghadimi, N., Akbarimajd, A., Shayeghi, H., Abedinia, O.: Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy 161, 130–142 (2018)

    Article  Google Scholar 

  34. Bagal, H.A., Soltanabad, Y.N., Dadjuo, M., Wakil, K., Ghadimi, N.: Risk-assessment of photovoltaic-wind-battery-grid based large industrial consumer using information gap decision theory. Sol. Energy 169, 343–352 (2018)

    Article  Google Scholar 

  35. Feng, S., Philip Chen, C.L.: Fuzzy broad learning system: a novel neuro-fuzzy model for regression and classification. IEEE Trans. Cybern. 50(2), 414–424 (2020)

    Article  Google Scholar 

  36. Khodaei, H., Hajiali, M., Darvishan, A., Sepehr, M., Ghadimi, N.: Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Appl. Therm. Eng. 137, 395–405 (2018)

    Article  Google Scholar 

  37. Uppal, A., Ray, W.H., Poore, A.B.: On the dynamic behavior of continuous stirred tank reactors. Chem. Eng. Sci. 29(4), 967–985 (1974)

    Article  Google Scholar 

  38. Trivedi, P.K., Bandyopadhyay, B., Mahata, S., Chaudhuri, S.: Roll stabilization: a higher-order sliding-mode approach. IEEE Trans. Aerosp. Electron. Syst. 51(3), 2489–2496 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Science Foundation of China under Grant 61973142, the Jiangsu Natural Science Foundation for Distinguished Young Scholars under Grant BK20180045, the PAPD of Jiangsu Higher Education Institutions and the Six Talent Peaks Project in Jiangsu Province under Grant XNYQC-006.

Author information

Authors and Affiliations

  1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China

    Qiankang Hou, Li Ma & Shihong Ding

  2. Discipline of Engineering and Energy, Murdoch University, Perth, WA, 6150, Australia

    Hai Wang

Authors
  1. Qiankang Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shihong Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shihong Ding.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hou, Q., Ma, L., Wang, H. et al. Fuzzy Disturbance Observer Design for a Class of Nonlinear SISO Systems. Int. J. Fuzzy Syst. 24, 147–158 (2022). https://doi.org/10.1007/s40815-021-01116-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-021-01116-8

Keywords

Search

Navigation