The hybrid adaptive control of T-S fuzzy system based on niche. (English) Zbl 1251.93076

Summary: Based on the niche characteristics, a hybrid adaptive fuzzy control method with the function of continuous supervisory control is proposed in this paper. Considering the close degree of Niche as the consequent of adaptive T-S fuzzy control system, the hybrid control law is designed by tracking, continuous supervisory, and adaptive compensation. Adaptive compensator is used in the controller to compensate the approximation error of fuzzy logic system and the effect of the external disturbance. The adaptive law of consequent parameters, which is achieved in this paper, embodies system adaptability as biological individual. It is proved that all signals in the closed-loop system are bounded and tracking error converges to zero by Lyapunov stability theory. The effectiveness of the approach is demonstrated by the simulation results.


93C42 Fuzzy control/observation systems
93C40 Adaptive control/observation systems
Full Text: DOI


[1] C. W. Chen, K. Yeh, and K. F. R. Liu, “Adaptive fuzzy sliding mode control for seismically excited bridges with lead rubber bearing isolation,” International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, vol. 17, no. 5, pp. 705-727, 2009. · Zbl 1186.93051
[2] B. Kosko, “Fuzzy systems as universal approximators,” IEEE Transactions on Computers, vol. 43, no. 11, pp. 1329-1333, 1994. · Zbl 1057.68664
[3] R. Rovatti, “Fuzzy piecewise multilinear and piecewise linear systems as universal approximators in sobolev norms,” IEEE Transactions on Fuzzy Systems, vol. 6, no. 2, pp. 235-249, 1998.
[4] L. X. Wang, A Course in Fuzzy Systems and Control, Prentice Hall, Englewood Cliffs, NJ, USA, 1997. · Zbl 0910.93002
[5] R. Shahnazi and M. R. Akbarzadeh-T, “PI adaptive fuzzy control with large and fast disturbance rejection for a class of uncertain nonlinear systems,” IEEE Transactions on Fuzzy Systems, vol. 16, no. 1, pp. 187-197, 2008. · Zbl 05516475
[6] L. X. Wang, “Stable adaptive fuzzy control of nonlinear systems,” IEEE Transactions on Fuzzy Systems, vol. 1, no. 2, pp. 146-155, 1993.
[7] P. A. Phan and T. J. Gale, “Direct adaptive fuzzy control with a self-structuring algorithm,” Fuzzy Sets and Systems, vol. 159, no. 8, pp. 871-899, 2008. · Zbl 1170.93345
[8] S. Labiod and T. M. Guerra, “Adaptive fuzzy control of a class of SISO nonaffine nonlinear systems,” Fuzzy Sets and Systems, vol. 158, no. 10, pp. 1126-1137, 2007. · Zbl 1113.93070
[9] J. H. Park, S. H. Huh, S. H. Kim, S. J. Seo, and G. T. Park, “Direct adaptive controller for nonaffine nonlinear systems using self-structuring neural networks,” IEEE Transactions on Neural Networks, vol. 16, no. 2, pp. 414-422, 2005.
[10] M. Wang, B. Chen, and S.-L. Dai, “Direct adaptive fuzzy tracking control for a class of perturbed strict-feedback nonlinear systems,” Fuzzy Sets and Systems, vol. 158, no. 24, pp. 2655-2670, 2007. · Zbl 1133.93350
[11] S. S. Ge and T. T. Han, “Semiglobal ISpS disturbance attenuation with output tracking via direct adaptive design,” IEEE Transactions on Neural Networks, vol. 18, no. 4, pp. 1129-1148, 2007.
[12] R. Qi and M. A. Brdys, “Stable indirect adaptive control based on discrete-time T-S fuzzy model,” Fuzzy Sets and Systems, vol. 159, no. 8, pp. 900-925, 2008. · Zbl 1170.93347
[13] C.-H. Hyun, C.-W. Park, and S. Kim, “Takagi-Sugeno fuzzy model based indirect adaptive fuzzy observer and controller design,” Information Sciences, vol. 180, no. 11, pp. 2314-2327, 2010. · Zbl 1214.93063
[14] S. Tong, H.-X. Li, and W. Wang, “Observer-based adaptive fuzzy control for SISO nonlinear systems,” Fuzzy Sets and Systems, vol. 148, no. 3, pp. 355-376, 2004. · Zbl 1057.93029
[15] Y. G. Leu, W. Y. Wang, and T. T. Lee, “Observer-based direct adaptive fuzzy-neural control for nonaffine nonlinear systems,” IEEE Transactions on Neural Networks, vol. 16, no. 4, pp. 853-861, 2005.
[16] M. K. Ciliz, “Combined direct and indirect adaptive control for a class of nonlinear systems,” IET Control Theory & Applications, vol. 3, no. 1, pp. 151-159, 2009.
[17] Y. Q. Zheng, Y. J. Liu, S. C. Tong, and T. S. Li, “Combined adaptive fuzzy control for uncertain MIMO nonlinear systems,” in Proceedings of the American Control Conference (ACC ’09), pp. 4266-4271, St. Louis, Mo, USA, June 2009.
[18] Q. Ding, H. Chen, C. Jiang, and Z. Chen, “Combined indirect and direct method for adaptive fuzzy output feedback control of nonlinear system,” Journal of Systems Engineering and Electronics, vol. 18, no. 1, pp. 120-124, 2007. · Zbl 1169.93356
[19] X. Ye and J. Huang, “Decentralized adaptive output regulation for a class of large-scale nonlinear systems,” IEEE Transactions on Automatic Control, vol. 48, no. 2, pp. 276-281, 2003. · Zbl 1364.93038
[20] N. Hovakimyan, E. Lavretsky, B. J. Yang, and A. J. Calise, “Coordinated decentralized adaptive output feedback control of interconnected systems,” IEEE Transactions on Neural Networks, vol. 16, no. 1, pp. 185-194, 2005.
[21] S. Tong, H. X. Li, and G. Chen, “Adaptive fuzzy decentralized control for a class of large-scale nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 34, no. 1, pp. 770-775, 2004.
[22] S. S. Stanković, D. M. Stipanović, and D. D. \vSiljak, “Decentralized dynamic output feedback for robust stabilization of a class of nonlinear interconnected systems,” Automatica, vol. 43, no. 5, pp. 861-867, 2007. · Zbl 1117.93057
[23] Z. Gao, T. Breikin, and H. Wang, “Reliable observer-based control against sensor failures for systems with time delays in both state and input,” IEEE Transactions on Systems, Man, and Cybernetics Part A, vol. 38, no. 5, pp. 1018-1029, 2008.
[24] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 1, pp. 116-132, 1985. · Zbl 0576.93021
[25] K. Yeh, C.-Y. Chen, and C.-W. Chen, “Robustness design of time-delay fuzzy systems using fuzzy Lyapunov method,” Applied Mathematics and Computation, vol. 205, no. 2, pp. 568-577, 2008. · Zbl 1152.93040
[26] C.-W. Chen, “The stability of an oceanic structure with T-S fuzzy models,” Mathematics and Computers in Simulation, vol. 80, no. 2, pp. 402-426, 2009. · Zbl 1174.86002
[27] Z. Gao and Y. Zhao, “Vehicle occupant classification algorithm based on T-S fuzzy model,” Procedia Engineering, vol. 24, pp. 500-504, 2011.
[28] Z. Gao, X. Shi, and S. X. Ding, “Fuzzy state/disturbance observer design for T-S fuzzy systems with application to sensor fault estimation,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 38, no. 3, pp. 875-880, 2008.
[29] A. L. Goldberger, “Fractal mechanisms in the electrophysiology of the heart,” IEEE Engineering in Medicine and Biology, vol. 11, no. 2, pp. 47-52, 1992.
[30] A. Babloyantz and A. Destexhe, “Is the normal heart a periodic oscillator?” Biological Cybernetics, vol. 58, no. 3, pp. 203-211, 1988.
[31] X.-M. Wang and L. I. Yi-min, “Kind of new niche fuzzy control method,” Science Technology and Engineering, vol. 24, pp. 6318-6321, 2007.
[32] S. P. Li, “Dynamical behavior analysis and control of two functional ecosystem,” Jiang Su University, pp. 33-43, 2005.
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