Multivariable Predictive Control Based on the T-S Fuzzy Model

Part of the Control Engineering book series (CONTRENGIN)


There are many complex industrial processes, such as the load control system of a power plant, that have nonlinear dynamics with time-varying parameters and with large time-delays. It is usually very difficult to design a satisfactory control system for such processes [7]. The adaptive control of nonlinear systems is one of the most often applied methods. In most cases, this approach is to transform nonlinear system dynamics into an appropriate linear model around an operating point, so that conventional linear control techniques can be applied [13]. A key assumption in these studies is that the system nonlinearities are known a priori and they are linearizable. Such an assumption limits the applications of the theory because real systems always contain uncertain disturbance and unmodeled dynamics. The design of a highly accurate modeling method for nonlinear systems and a nonlinear model-based adaptive control methods helps to deal with these limitations.


Fuzzy Control Model Predictive Control Fuzzy Control System Transfer Function Matrix Generalize Predictive Control 
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  1. [1]
    J. Buckley, “Sugeno-type controllers are universal controllers,” Fuzzy Sets and Systems, vol. 52, no. 2, pp. 299–303, 1993.CrossRefGoogle Scholar
  2. [2]
    S. Cao, “Analysis and design for a class of complex control systems, Part II: Fuzzy controller design”, Automatica, vol. 33. pp. 1029–1039, 1997.MATHCrossRefGoogle Scholar
  3. [3]
    L. Chen, Automatic Control Principle for the Thermal Process and Its Applications, China Power Industry Press, China, 1991. (in Chinese)Google Scholar
  4. [4]
    D. Clark, C. Mohtadi, and P. Toffs, “Generalized predictive control. Part I: The basic algorithm”, Automatica, vol. 23, no. 1, pp. 137–148, 1987.CrossRefGoogle Scholar
  5. [5]
    C. Dai, Linear Algebra in Control Systems, Southeastern University Press, Nanjing, China, 1993. (in Chinese)Google Scholar
  6. [6]
    T. Hansan, T. Fevzullah, and Y. Nejat, “Neural generalized predictive control: Robotic manipulators with cubic and sinusoidal trajectory”, Proc. XII International Turkish Symposium on Artificial Intelligence and Neural Networks, Tainn, Turkey, July 2003, pp. 124–132.Google Scholar
  7. [7]
    O. Hecker, “Nonlinear system identification and predictive control of a heat exchanger”, Proc. American Control Conference, 1997, pp. 3294–3298.Google Scholar
  8. [8]
    J. Rawlings, “Tutorial overview of model predictive control”, IEEE Control Systems Magazine, vol. 20, no. 1, pp. 38–52, 2000.CrossRefMathSciNetGoogle Scholar
  9. [9]
    M. E. Sezer and D. D. Siljak, “Stability of interval matrices”, IEEE Transactions on Automatic Control, vol. 39, pp. 368–371, 1994.MATHCrossRefMathSciNetGoogle Scholar
  10. [10]
    J. T. Spooner, “Direct adaptive fuzzy control for a class of discrete-time systems”, Proc. American Control Conference, 1997, pp. 1814–1818.Google Scholar
  11. [11]
    K. Tanaka, “Robust stabilization of a class of uncertain nonlinear systems via fuzzy control: Quadratic stability, control theory and linear matrix inequalities”, IEEE Transactions on Fuzzy Systems, vol. 4, no. 1, pp. 1–14, 1996.CrossRefGoogle Scholar
  12. [12]
    K. Tanaka and M. Sugeno, “Stability analysis and design of fuzzy control systems”, Fuzzy Sets and Systems, vol. 45, no. 1, pp. 135–156, 1992.MATHCrossRefMathSciNetGoogle Scholar
  13. [13]
    S. Tong, J. Tang, and T. Wang, “Fuzzy adaptive control of multivariable nonlinear systems”, Fuzzy Sets and Systems, vol. 111, pp. 153–167, 2000.MATHCrossRefMathSciNetGoogle Scholar
  14. [14]
    J. Waller, J. Hu, and K. Hirasawa, “Nonlinear model predictive control utilizing a neuro-fuzzy predictor”, Proc. IEEE Conference on Systems, Man, and Cybernetics, Nashville, TN, 2000, pp. 3459–3464.Google Scholar
  15. [15]
    L. Wang, “Design and analysis of fuzzy identifiers of nonlinear dynamic systems”, IEEE Transactions on Automatic Control, vol. 40, no. 1, pp. 111–117, 1995.MATHCrossRefGoogle Scholar
  16. [16]
    H. Zhang, “Fuzzy generalized predictive control and its applications”, ACTA Automatica Sinica, vol. 19, no. 1, pp. 9–17, 1993. (in Chinese)MATHGoogle Scholar
  17. [17]
    H. Zhang and L. Cai, “Multivariable fuzzy generalized predictive control for general nonlinear SISO systems”, Cybernetics and Systems, vol. 33, no. 1, pp. 69–99, 2002.MATHCrossRefGoogle Scholar
  18. [18]
    H. Zhang, L. Cai, and Z. Bien, “A fuzzy basis function vector-based multivariable adaptive fuzzy controller for nonlinear systems”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 30, no. 1, pp. 210–217, Feb. 2000.CrossRefGoogle Scholar
  19. [19]
    H. Zhang, L. Cai, and Z. Bien, “A multivariable generalized predictive control approach based on T—S fuzzy model”, Journal of Intelligent and Fuzzy Systems, vol. 9, no. 3, pp. 169–190, 2000.Google Scholar
  20. [20]
    H. Zhang and T. Chai, “Fuzzy modeling of operators’ control rules and its application”, ACTA Automatica Sinica, vol. 20, no. 3, pp. 308–315, 1994. (in Chinese)MathSciNetGoogle Scholar

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