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Design and analysis of neural/fuzzy variable structural PID control systems

Design and analysis of neural/fuzzy variable structural PID control systems

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The paper describes the design method of a neural/fuzzy variable structural proportional-integral-derivative (neural/fuzzy VSPID) control system. The neural/fuzzy VSPID controller has a structure similar to that of the conventional PID. In this controller, the PD mode is used in the case of large errors to speed up response, whereas the PI mode is applied for small error conditions to eliminate the steady-state offset. A sigmoidal-like neuron is employed as a preassigned algorithm of the law of structural change. Meanwhile, the controller parameters would be changed according to local conditions. Bounded neural networks or bounded fuzzy logic systems are used for constructing the nonlinear relationship between the PID controller parameters and local operating control conditions. Flexible changes of controller modes and resilient controller parameters of the neural/fuzzy VSPID during the transient could thereby solve the typical conflict in nature between steady-state error and dynamic responsiveness. A neutralisation process is used to demonstrate the applicability of such a controller for controlling highly nonlinear processes.

References

    1. 1)
      • F.G. Shinskey . (1988) Process control systems: application, design, and tuning.
    2. 2)
      • F.C. Clark . PID algorithms and their computer implementation. Trans. Inst. Meas. Control , 305 - 316
    3. 3)
      • A. Jutan . A nonlinear PI(D) controller. Can. J. Chem. Eng. , 485 - 493
    4. 4)
      • S. Tan , Yu. Lin , P. Wang , S. He . Objective-centered formulation of an adaptive fuzzy control scheme. Int. J. Uncertainty, Fuzziness and Knowledge-Based Syst. , 3 , 321 - 331
    5. 5)
      • W.C. Chen , C.L. Chen . Nonlinear PI controller design: a neural network approach. J. Chin. I. Chem. E. , 2 , 67 - 79
    6. 6)
      • H. Wang , M. Brown , C.J. Harris . Neural network modelling of unknown nonlinear systems subject to immeasurabledisturbances. IEE Proc., Control Theory Appl. , 4 , 216 - 222
    7. 7)
      • G. Lightbody , G.W. Irwin . Direct neural model reference adaptive control. IEE Proc., Control Theory Appl. , 1 , 31 - 43
    8. 8)
      • J.R. Raol . Neural network based parameter estimation of unstable aerospace dynamicsystems. IEE Proc., Control Theory Appl. , 6 , 385 - 388
    9. 9)
      • Y.Y. Yang , D.A. Linkens . Adaptive neural-network-based approach for the control of continuouslystirred tank reactor. IEE Proc., Control Theory Appl. , 5 , 341 - 349
    10. 10)
      • S. Horikawa , T. Furuhashi , Y. Uchikawa . On fuzzy modelling using fuzzy neural networks with backpropagation algorithm. IEEE Trans. Neural Netw. , 801 - 806
    11. 11)
      • L.X. Wang . (1994) Adaptive fuzzy systems and control.
    12. 12)
      • J. Park , I.W. Sandberg . Approximation and radial-basis-function networks. Neural Comput. , 305 - 316
    13. 13)
      • J. Moody , C.J. Darken . Fast learning in networks of locally-tuned processing units. Neural Comput. , 281 - 294
    14. 14)
      • D.E. Rumelhart , G.E. Hinton , R.J. Williams , D.E. Rumelhart , J.L. McClelland . (1986) Learning internal representations by error propagation, Parallel distributed processing: explorations in the microstructuresof cognition. Vol.1: foundations.
    15. 15)
      • S.N. Kavuri , V. Venkatasubramanian . Using fuzzy clustering with ellipsoidal units in neural networks forrobust fault classification. Comput. Chem. Eng. , 8 , 765 - 784
    16. 16)
      • S. CHEN , C. COWAN , P. GRANT . Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Netw. , 302 - 309
    17. 17)
      • C.L. Chen , W.C. Chen , F.Y. Chang . Hybrid learning algorithm for Gaussian potential function networks. IEE Proc. D , 6 , 442 - 448
    18. 18)
      • Powell, M.J.D.: `Radial basis function for multivariate interpolation: a review', DAMPT 1985/NA12, Technical report, 1985.
    19. 19)
      • M. Vidyasagar . (1978) Nonlinear system analysis.
    20. 20)
      • D. Psaltis , A. Sideris , A.A. Yamamura . A multilayered neural network controller. IEEE Control Syst. Mag. , 17 - 21
    21. 21)
      • D.H. Nguyen , B. Widrow . Neural networks for self-learning control system. IEEE Control Syst. Mag. , 18 - 23
    22. 22)
      • F.C. Chen . Back-propagation for nonlinear self-tuning adaptive control. IEEE Control Syst. Mag. , 44 - 48
    23. 23)
      • T.J. McAvoy , E. Hsu , S. Lowenthal . Dynamics of pH in controlled stirred tank reactor. Ind. Eng. Chem. Process Des. Develop. , 68 - 70
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