FOPTD model parameters of a centralised (PI) controlled multivariable system are identified by a closed-loop optimisation method. The model parameters are obtained by a non-linear least square minimisation method. An easy procedure is given for the guess values of the parameters. For arbitrary guess values, the minimisation method does not converge. The simulation studies are given for several transfer function matrices. © 2016 Indian Institute of Chemical Engineers.

V. Dhanya Ram

Manickam Chidambaram

Multivariable Systems

CENTRALISED

Initial guess

Least square methods

Model identification

Multi variables

Least squares approximations

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IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

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IIT Madras

Identification of Centralised Controlled Multivariable Systems

Indian Chemical Engineer

The closed-loop identification of second-order plus time delay (SOPTD) transfer function models of multivariable systems is presented based on optimization method using the combined step-up and step-down responses. The need for combined step up and step down changes in the set point is brought out for the convergence of the model parameters. A standard nonlinear least-squares optimization method is used to obtain the parameters of the SOPTD model transfer function matrix by minimizing the sum of squared errors between the closed-loop responses of the model and the actual process responses. A simple method is proposed to obtain the initial guess values of SOPTD transfer function model parameters from the main and interaction responses of the actual process. This method was applied to two-input two-output (TITO) second-order plus time delay (SOPTD), higher order and 3 × 3 SOPTD transfer function models of multivariable systems. The proposed method considerably reduces the computational time for the optimization for 2 × 2 SOPTD model systems (11 min and 11 s) when compared with the genetic algorithm (GA) method reported by Viswanathan et al. (Ind. Eng. Chem. Res.2001, 40, 2818-2826). © 2012 American Chemical Society.

Industrial and Engineering Chemistry Research

Closed-loop identification of second-order plus time delay (SOPTD) model of multivariable systems by optimization method

A method for the independent design of proportional-integral/proportional- integral derivative (PI/PID) controllers is proposed based on the equivalent transfer function (ETF) model of the individual loops and the simplified decoupler matrix. It is shown that the conventional effective open-loop transfer function (EOTF, derived from the dynamic relative gain array (DRGA)) is equivalent to the ETF (derived from the relative normalized gain array (RNGA) and relative average residence time array (RARTA)). This relation is used to approximate the decoupled process models as ETF models. The simplified decoupler is shown to decompose the multiloop systems into independent loops (multi-single loop systems) with the ETFs as the resulting decoupled process model. The concept of the ETF (perfect control approximation) is validated by introducing the decoupler. Based on the corresponding ETFs, the decentralized PI controllers are designed using the simplified internal model control (SIMC) method. Three simulation examples of multi-input multi-output (MIMO) process models are considered to demonstrate the simplicity and effectiveness of the proposed method. The performance of the proposed control system is compared with the ideal, normalized, inverted decoupling, and centralized control systems. © 2012 American Chemical Society.

Controller design for MIMO processes based on simple decoupled equivalent transfer functions and simplified decoupler

An optimization method is presented for the closed-loop identification of first-order-plus-time-delay (FOPTD) transfer function models of multivariable systems using step responses. A standard least-squares optimization method is used to obtain the parameters of the FOPTD models by matching the closed-loop step responses of the model with those of the actual process. A simple method is proposed to obtain the initial guess values for the transfer function model parameters from the process main and interaction responses. The effects of measurement noise and controller settings on the identified model parameters were also studied. This method was applied to stable FOPTD and higher-order transfer function models of multivariable systems. The proposed method considerably reduces the computational time (by about a factor of 15) for the optimization when compared with the genetic algorithm method reported by Viswanathan et al. (Ind. Eng. Chem. Res. 2001, 40, 2818-2826). © 2011 American Chemical Society.

Closed-loop identification of multivariable systems by optimization method

In this article, two methods of designing a centralized control system for multi-input, multi-output (MIMO) processes are presented. Centralized proportional-integral (PI) controllers are designed based on a direct synthesis method. The inverse of the process transfer function matrix in the direct synthesis method is approximated based on the relative gain array concept. The method is further improved by using a relative normalized gain array, and an equivalent transfer function for each element in the process transfer function matrix is derived for the closed-loop control system. The transpose of the effective transfer function is used to approximate the inverse of the process transfer function matrix. The simulation studies demonstrate the effectiveness of this method. The proposed centralized controllers reduce the interactions better than recently reported decentralized controllers do. A centralized controller designed based on a relative normalized gain array (RNGA) gives a better performance than a centralized controller designed based on a relative gain array (RGA). © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Journal	Data powered by TypesetIndian Chemical Engineer
Publisher	Data powered by TypesetTaylor and Francis Ltd.
ISSN	00194506
Open Access	No