An integrated identification and control design methodology for multivariable process system applications

We present a way to take advantage of the favorable asymptotic properties of ARX estimators to develop an integrated methodology for identification and controller design for multivariable process plants. This method relies on well-established numerical tools and builds on an engineer's existing...

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Bibliographic Details
Published in:IEEE Control Systems Magazine 2000-06, Vol.20 (3), p.25-37
Main Authors: Rivera, D.E., Jun, K.S.
Format: Article
Language:English
Subjects:
Binary sequences
Control design
Control systems
Design engineering
Distillation
Guidelines
Knowledge engineering
Mathematical models
Methodology
MIMO
Multivariable
Multivariable control
Process control
Process design
Signal design
Signal processing
Tuning
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Summary:We present a way to take advantage of the favorable asymptotic properties of ARX estimators to develop an integrated methodology for identification and controller design for multivariable process plants. This method relies on well-established numerical tools and builds on an engineer's existing process and statistical intuition. Specifically, the ARX estimate serves as a suitable intermediate model for the design and analysis of MIMO process control systems. Guidelines for the design of pseudo-random binary sequence signals that take advantage of the engineer's prior knowledge of the process time constants are presented. Control-relevant model reduction is performed on elements of the ARX model to obtain low-order models conforming to the IMC-PID tuning rules. A simple analysis technique is used to assess stability of the decentralized and decoupled strategies. These techniques and full multivariable control are applied to the Shell heavy oil fractionator problem and the Weischedel-McAvoy distillation column model, respectively.
ISSN:1066-033X
0272-1708
1941-000X
DOI:10.1109/37.845036