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Wiener model identification and predictive control of a pH neutralisation process

Wiener model identification and predictive control of a pH neutralisation process

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Wiener model identification and predictive control of a pH neutralisation process is presented. Input-output data from a nonlinear, first principles simulation model of the pH neutralisation process are used for subspace-based identification of a black-box Wiener-type model. The proposed nonlinear subspace identification method has the advantage of delivering a Wiener model in a format which is suitable for its use in a standard linear-model-based predictive control scheme. The identified Wiener model is used as the internal model in a model predictive controller (MPC) which is used to control the nonlinear white-box simulation model. To account for the unmeasurable disturbance, a nonlinear observer is proposed. The performance of the Wiener model predictive control (WMPC) is compared with that of a linear MPC, and with a more traditional feedback control, namely a PID control. Simulation results show that the WMPC outperforms the linear MPC and the PID controllers.

References

    1. 1)
      • Adaptive nonlinear control of a pH neutralization process
    2. 2)
      • Nonlinear process control
    3. 3)
      • An adaptive internal model control strategy for pH neutralization
    4. 4)
      • Nonlinear control of pH processes using the strong acid equivalent
    5. 5)
      • Predictive control with constraints
    6. 6)
      • Nonlinear model predictive control: current status and future directions
    7. 7)
      • A nonlinear predictive control strategy based on radial basis functions models
    8. 8)
      • Nonlinear model predictive control using Hammerstein models
    9. 9)
      • Constrained nonlinear MPC using Hammerstein and Wiener models: PLS framerwork
    10. 10)
      • Model predictive control based on Wiener models
    11. 11)
      • Application of Wiener Model Predictive Control (WMPC) to a pH neutralization experiment
    12. 12)
      • A new approach to the identification of pH processes based on the Wiener model
    13. 13)
      • Fading memory and the problem of approximating nonlinear operators with Volterra series
    14. 14)
      • Wiener model identification and predictive control for dual composition control of a distillation column
    15. 15)
      • Nonparametric identification of Wiener systems by orthogonal series
    16. 16)
      • Recursive prediction error identification using the nonlinear Wiener model
    17. 17)
      • Identification of systems containing linear dynamic and static nonlinear elements
    18. 18)
      • N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems
    19. 19)
      • Identification of the deterministic part of MIMO state space models given in innovations form from input-out data
    20. 20)
      • Larimore, W.: `Canonical variate analysis in identification, filtering, and adaptive control', Proc. 29th IEEE Conf. on Decision and Control, Honolulu, HI, December 1990, p. 596–604
    21. 21)
      • Identifying MIMO Wiener systems using subspec model identification methods
    22. 22)
      • Identifying MIMO Hammerstein systems in the context of subspace model identification methods
    23. 23)
      • Gómez, J.C., Baeyens, E.: `Subspace identification of multivarial Hammerstein and Wiener models', Proc. 15th International Federation of Automatic Control World Congress, Barcelona, Spain, July 2002, p. 2849–2854
    24. 24)
      • A nonlinear observer for estimating parameters, in dynamic systems
    25. 25)
      • Adaptive control - stability convergence and robustness
    26. 26)
      • Nonlinear control systems
    27. 27)
      • System identification: toolbar, user's guide, ver. 5
    28. 28)
      • System identification: theory for the user
    29. 29)
      • Model predictive control
    30. 30)
      • Model predictive control toolbox-for use with Matlab, user's guide
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