Fuzzy-Model-Based Non-fragile GCC of Fuzzy MJSs

  • Shanling DongEmail author
  • Zheng-Guang Wu
  • Peng Shi
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 268)


Recent years have witnessed rising interest in the GCC problem [1, 2], whose main aim is to make a studied system stable with an adequate level of performance via devising appropriate control laws. The work in [3] has investigated the GCC issue for descriptor systems with uncertainties and robust normalization. For networked control systems, the work in [4] has focused on the output feedback GCC problem with consideration of time delays as well as stochastic packet dropouts.


  1. 1.
    Chang, S.S., Peng, T.: Adaptive guaranteed cost control of systems with uncertain parameters. IEEE Trans. Autom. Control 17(4), 474–483 (1972)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Pang, B., Liu, X., Jin, Q., Zhang, W.: Exponentially stable guaranteed cost control for continuous and discrete-time takagicsugeno fuzzy systems. Neurocomputing 205, 210–221 (2016)CrossRefGoogle Scholar
  3. 3.
    Ren, J., Zhang, Q.: Robust normalization and guaranteed cost control for a class of uncertain descriptor systems. Automatica 48(8), 1693–1697 (2012)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Qiu, L., Yao, F., Xu, G., Li, S., Xu, B.: Output feedback guaranteed cost control for networked control systems with random packet dropouts and time delays in forward and feedback communication links. IEEE Trans. Autom. Sci. Eng. 13(1), 284–295 (2016)CrossRefGoogle Scholar
  5. 5.
    Lu, R., Cheng, H., Bai, J.: Fuzzy-model-based quantized guaranteed cost control of nonlinear networked systems. IEEE Trans. Fuzzy Syst. 23(3), 567–575 (2015)CrossRefGoogle Scholar
  6. 6.
    Han, C., Wu, L., Lam, H.K., Zeng, Q.: Nonfragile control with guaranteed cost of T-S fuzzy singular systems based on parallel distributed compensation. IEEE Trans. Fuzzy Syst. 22(5), 1183–1196 (2014)CrossRefGoogle Scholar
  7. 7.
    Li, X., Gao, H., Gu, K.: Delay-independent stability analysis of linear time-delay systems based on frequency discretization. Automatica 70, 288–294 (2016)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Li, Q.-K., Lin, H.: Effects of mixed-modes on the stability analysis of switched time-varying delay systems. IEEE Trans. Autom. Control 61(10), 3038–3044 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985)CrossRefGoogle Scholar
  10. 10.
    Himavathi, S., Umamaheswari, B.: New membership functions for effective design and implementation of fuzzy systems. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 31(6), 717–723 (2001)CrossRefGoogle Scholar
  11. 11.
    Tao, J., Lu, R., Shi, P., Su, H., Wu, Z.-G.: Dissipativity-based reliable control for fuzzy Markov jump systems with actuator faults. IEEE Trans. Cybern. 47(9), 2377–2388 (2017)CrossRefGoogle Scholar
  12. 12.
    Zhang, L., Ning, Z., Shi, P.: Input-output approach to control for fuzzy Markov jump systems with time-varying delays and uncertain packet dropout rate. IEEE Trans. Cybern. 45(11), 2449–2460 (2015)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Laboratory of Industrial Control TechnologyInstitute of Cyber-Systems and Control, Zhejiang UniversityHangzhouChina
  2. 2.School of Electrical and Electronic EngineeringUniversity of AdelaideAdelaideAustralia
  3. 3.Victoria UniversityMelbourneAustralia

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