Comparing MIMO Process Control Methods on a Pilot Plant

Comparing MIMO Process Control Methods on a Pilot Plant This work presents a comparison among three different control strategies for multivariable processes. The techniques were implemented in a pilot plant with coupled control loops, where all steps used to design the controllers were described allowing to establish a trade-off between algorithm complexity, information needed from the process and achieved performance. Two data-driven control techniques are used: multivariable ultimate point method to design a decentralized PID controller and virtual reference feedback tuning to design a centralized PID controller. A mathematical model of the process is obtained and used to design a model-based generalized predictive controller. Experimental results allow us to evaluate the performance achieved for each method, as well as to infer on their advantages and disadvantages. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Control, Automation and Electrical Systems Springer Journals

Comparing MIMO Process Control Methods on a Pilot Plant

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Brazilian Society for Automatics--SBA
Subject
Engineering; Electrical Engineering; Control, Robotics, Mechatronics; Control; Robotics and Automation
ISSN
2195-3880
eISSN
2195-3899
D.O.I.
10.1007/s40313-018-0387-6
Publisher site
See Article on Publisher Site

Abstract

This work presents a comparison among three different control strategies for multivariable processes. The techniques were implemented in a pilot plant with coupled control loops, where all steps used to design the controllers were described allowing to establish a trade-off between algorithm complexity, information needed from the process and achieved performance. Two data-driven control techniques are used: multivariable ultimate point method to design a decentralized PID controller and virtual reference feedback tuning to design a centralized PID controller. A mathematical model of the process is obtained and used to design a model-based generalized predictive controller. Experimental results allow us to evaluate the performance achieved for each method, as well as to infer on their advantages and disadvantages.

Journal

Journal of Control, Automation and Electrical SystemsSpringer Journals

Published: May 29, 2018

References

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