Wiener model identification and predictive control of a pH neutralisation process

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.

[1]  Bernard Friedland,et al.  A nonlinear observer for estimating parameters in dynamic systems , 1997, Autom..

[2]  Sirish L. Shah,et al.  Constrained nonlinear MPC using hammerstein and wiener models: PLS framework , 1998 .

[3]  Torbjörn Wigren,et al.  Recursive prediction error identification using the nonlinear wiener model , 1993, Autom..

[4]  Gade Pandu Rangaiah,et al.  An adaptive internal model control strategy for pH neutralization , 1997 .

[5]  Dale E. Seborg,et al.  Adaptive nonlinear control of a pH neutralization process , 1994, IEEE Trans. Control. Syst. Technol..

[6]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[7]  Michel Verhaegen,et al.  Identifying MIMO Wiener systems using subspace model identification methods , 1996, Signal Process..

[8]  Wallace E. Larimore,et al.  Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.

[9]  Marko Bacic,et al.  Model predictive control , 2003 .

[10]  R. A. Wright,et al.  Nonlinear control of pH processes using the strong acid equivalent , 1991 .

[11]  M. Verhaegen,et al.  Identifying MIMO Hammerstein systems in the context of subspace model identification methods , 1996 .

[12]  Michael A. Henson,et al.  Nonlinear model predictive control: current status and future directions , 1998 .

[13]  A. Palazoglu,et al.  Nolinear model predictive control using Hammerstein models , 1997 .

[14]  Dale E. Seborg,et al.  Nonlinear Process Control , 1996 .

[15]  S. Sastry,et al.  Adaptive Control: Stability, Convergence and Robustness , 1989 .

[16]  Ahmet Palazoglu,et al.  Model predictive control based on Wiener models , 1998 .

[17]  Leon O. Chua,et al.  Fading memory and the problem of approximating nonlinear operators with volterra series , 1985 .

[18]  Stephen A. Billings,et al.  Identification of systems containing linear dynamic and static nonlinear elements , 1982, Autom..

[19]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[20]  Dale E. Seborg,et al.  A nonlinear predictive control strategy based on radial basis function models , 1997 .

[21]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[22]  José A. Romagnoli,et al.  Application of Wiener model predictive control (WMPC) to a pH neutralization experiment , 1999, IEEE Trans. Control. Syst. Technol..

[23]  W. R. Cluett,et al.  A new approach to the identification of pH processes based on the Wiener model , 1995 .

[24]  E. Baeyens,et al.  SUBSPACE IDENTIFICATION OF MULTIVARIABLE HAMMERSTEIN AND WIENER MODELS , 2002 .

[25]  H. Bloemen,et al.  Wiener Model Identification and Predictive Control for Dual Composition Control of a Distillation Column , 2001 .

[26]  W. Greblicki.  Nonparametric identification of Wiener systems by orthogonal series , 1994, IEEE Trans. Autom. Control..

[27]  Michel Verhaegen,et al.  Identification of the deterministic part of MIMO state space models given in innovations form from input-output data , 1994, Autom..