Multi-objective input signal design for plant friendly identification of process systems

System identification is the process of constructing an accurate and reliable dynamic mathematical model of the system from observed data and available knowledge. The choice of inputs used for perturbing the system is critical in the identification and model building exercise. One of the major objectives of system identification is accurate estimation of the system parameters. Identification of chemical process plants is carried out on running plants in real time. The practitioner would thus prefer a 'plant friendly' input signal. We propose unified multi-objective formulations and solution methods for the input design for two particular cases. The input can be evaluated as a solution to a multi-objective optimization problem.

[1]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[2]  Hans D. Mittelmann,et al.  CONSTRAINED MULTISINE INPUTS FOR PLANT-FRIENDLY IDENTIFICATION OF CHEMICAL PROCESSES , 2002 .

[3]  D.G. Dudley,et al.  Dynamic system identification experiment design and data analysis , 1979, Proceedings of the IEEE.

[4]  Pll Siinksen,et al.  Control , 1999, Diabetic medicine : a journal of the British Diabetic Association.

[5]  Martin B. Zarrop,et al.  Optimal experiment design for dynamic system identification , 1977 .

[6]  Raghunathan Rengaswamy,et al.  Multi-objective input signal design for plant-friendly identification , 2003 .

[7]  Tor Arne Johansen,et al.  Identification of non-linear systems using empirical data and prior knowledge - an optimization approach , 1996, Autom..

[8]  J. Schoukens,et al.  Crest-factor minimization using nonlinear Chebyshev approximation methods , 1991 .

[9]  Cecil L. Smith Intelligently tune PID controllers , 2003 .

[10]  L. Goddard Approximation of Functions , 1965, Nature.

[11]  Brian D. O. Anderson,et al.  On the choice of inputs in identification for robust control , 1999, Autom..

[12]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[13]  M. S. Salim,et al.  Numerical treatment of multiobjective optimal control problems , 2003, Autom..

[14]  Francis J. Doyle,et al.  The identification of nonlinear models for process control using tailored “plant-friendly” input sequences , 2001 .

[15]  R. K. Mehra,et al.  CHOICE OF INPUT SIGNALS , 1981 .

[16]  Narendra Ahuja,et al.  TWO-STEP APPROACH TO OPTIMAL MOTION AND STRUCTURE ESTIMATION. , 1987 .

[17]  Roland Hildebrand,et al.  Identification for control: optimal input design with respect to a worst-case /spl nu/-gap cost function , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[18]  T. Johansen Multi-Objective Identification of FIR Models , 2000 .

[19]  Keith R. Godfrey,et al.  Perturbation signals for system identification , 1993 .

[20]  Lennart Ljung,et al.  Optimal experiment designs with respect to the intended model application , 1986, Autom..

[21]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.