Continuous-discrete time prediction-error identification relevant for linear model predictive control

A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model is to be applied. The suitability of the proposed prediction error-method for predictive control is demonstrated for dual composition control of a simulated binary distillation column.

[1]  James B. Rawlings,et al.  Tutorial overview of model predictive control , 2000 .

[2]  R. K. Wood,et al.  Terminal composition control of a binary distillation column , 1973 .

[3]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[4]  James B. Rawlings,et al.  Constrained linear quadratic regulation , 1998, IEEE Trans. Autom. Control..

[5]  James B. Rawlings,et al.  Model predictive control with linear models , 1993 .

[6]  J. Richalet,et al.  Model predictive heuristic control: Applications to industrial processes , 1978, Autom..

[7]  C. R. Cutler,et al.  Dynamic matrix control¿A computer control algorithm , 1979 .

[8]  John F. MacGregor,et al.  Identification for robust multivariable control: The design of experiments , 1994, Autom..

[9]  Sten Bay Jørgensen,et al.  Recent Advances and Challenges in Process Identification , 2002 .

[10]  Michel Gevers,et al.  Identification for Control: Achievements and Open Problems 1 , 2004 .

[11]  Sirish L. Shah,et al.  The nature of data pre-filters in MPC relevant identification - open- and closed-loop issues , 2003, Autom..

[12]  S. Shah,et al.  A control-relevant identification strategy for GPC , 1992 .

[13]  Michel Gevers,et al.  Identification for Control: Achievements and Open Problems 1 , 2004 .

[14]  Karl Johan Åström,et al.  Maximum likelihood and prediction error methods , 1979, Autom..

[15]  J. A. Rossiter,et al.  Modelling and implicit modelling for predictive control , 2001 .

[16]  Robert Haber,et al.  Long-range optimal model and multi-step-ahead prediction identification for predictive control , 2003 .

[17]  Babatunde A. Ogunnaike,et al.  Process Dynamics, Modeling, and Control , 1994 .

[18]  Sten Bay Jørgensen,et al.  Parameter estimation in stochastic grey-box models , 2004, Autom..

[19]  Edoardo Mosca,et al.  Performance improvements of self-tuning controllers by multistep horizons: The MUSMAR approach , 1984, Autom..

[20]  Kenneth R. Muske,et al.  Disturbance modeling for offset-free linear model predictive control , 2002 .

[21]  N. L. Ricker,et al.  Adaptive optimal control: The thinking man's GPC : R. R. Bitmead, M. Gevers and V. Wertz , 1993, Autom..

[22]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

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

[24]  Håkan Hjalmarsson,et al.  From experiments to closed loop control , 2003 .

[25]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[26]  V. Wertz,et al.  Adaptive Optimal Control: The Thinking Man's G.P.C. , 1991 .

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

[28]  Arye Nehorai,et al.  On multistep prediction error methods for time series models , 1989 .

[29]  Sirish L. Shah,et al.  MPC relevant identification––tuning the noise model , 2004 .

[30]  Gabriele Pannocchia,et al.  Disturbance models for offset‐free model‐predictive control , 2003 .

[31]  E. Mosca.  Optimal, Predictive and Adaptive Control , 1994 .

[32]  Jay H. Lee,et al.  Model predictive control: past, present and future , 1999 .

[33]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[34]  Sten Bay Jørgensen,et al.  Dynamics and identification of a binary distillation column , 1989 .

[35]  Yucai Zhu,et al.  Multivariable System Identification For Process Control , 2001 .

[36]  J. B. Jørgensen,et al.  Numerical Methods for Large Scale Moving Horizon Estimation and Control , 2004 .

[37]  Michel Gevers,et al.  A personal view on the development of system identification 1 , 2003 .