Feedforward Fuzzy Control Approach Using the Fourier Integral

Part of the Control Engineering book series (CONTRENGIN)


The most effective method to compensate for the effect of measured disturbances is to use the techniques of feedforward control [1]. It attempts to produce a control action with an effect equal and opposite to the effect of the disturbance. Crisafulli [3] presented a moving average identification technique to model the main process disturbance factors and then formed a feedforward control system to control the surge tanks. Gorinevsky, et al. [4] proposed a learning feedforward control for a robot manipulator tracking using B-spline to approximate the input shaping function. However, the exact mathematical model from the source of disturbances to the output is very difficult to obtain on-line, which limits the application of the feedforward control technique. In fact, the feedforward control law can be obtained by function mapping (FM) that takes current (and possibly past) information about the system and produces control actions to affect future system performance. In this chapter, the Fourier integral is used for the FM and a novel Fourier integral-based adaptive method is used for the feedforward controller, while the fuzzy sliding mode control (FSMC) method is used for the feedback controller.


Fuzzy Logic Controller Fourier Space Feedforward Control Fuzzy Logic Control Feedforward Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    C. Anton, E. Johan, B. Bo, and A. Karl-Erik, “Feedback-feedforward scheduling of control tasks.” Real-Time Systems, vol. 23, no. 1, pp. 25–53, 2002.MATHCrossRefGoogle Scholar
  2. [2]
    B. Choi, S. Kwak, and B. Kim. “Design of a single-input fuzzy logic controller and its properties,” Fuzzy Sets and Systems, vol. 3, no. 106, pp. 299–308, 1999.CrossRefMathSciNetGoogle Scholar
  3. [3]
    S. Crisafulli, “Surge tank control in a cane raw sugar factory,” Journal of Process Control, vol. 1, no. 9, pp. 33–39, 1999.CrossRefGoogle Scholar
  4. [4]
    D. Gorinevsky, D. Torfs, and A. Goldenberg, “Learning approximation of feedforward control dependence on the task parameters with application to direct-drive manipulator tracking,” IEEE Transactions on Robotics and Automation, vol. 13, no. 14, pp. 567–581, 1997.CrossRefGoogle Scholar
  5. [5]
    C. Kwan, “Sliding mode control of linear systems with mismatched uncertainties,” Automatica, vol. 2, no. 31, pp. 303–307, 1995.CrossRefMathSciNetGoogle Scholar
  6. [6]
    E. Rayad, “Variable structure robust control of uncertain time-delay systems,” Automatica, vol. 3, no. 34, pp. 327–332, 1998.Google Scholar
  7. [7]
    H. Zhang and L. Cai, “Nonlinear adaptive control using the Fourier integral and its application to CSTR systems,” IEEE Transactions on Systems, Man and Cybernetics-Part B, vol. 32, no. 3, pp. 367–372, 2002.CrossRefGoogle Scholar
  8. [8]
    H. Zhang and L. Cai, “Decentralized nonlinear control of a HVAC system,” IEEE Transactions on Systems, Man, and Cybernetics-Part C, vol. 32, no. 4, pp. 493–498, 2002.CrossRefGoogle Scholar
  9. [9]
    H. Zhang and L. Cai, “Fuzzy adaptive control of SISO nonlinear processes with feedforward compensator and its application to superheated steam temperature,” Cybernetics and Systems, vol. 33, no. 2, pp. 171–187, 2002.MATHCrossRefGoogle Scholar
  10. [10]
    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. 3, no. 9, pp. 169–190, 2000.Google Scholar

Copyright information

© Birkhäuser Boston 2006

Personalised recommendations