Model predictive control design for industrial applications

L. Balbis, R. Kateb, A.W. Ordys

Research output: Contribution to conferencePaper

Abstract

In many industrial processes, the regulatory level based on PID controllers is
able to maintain the process variables about the given set point values. However,
economic reasons and operational constraints make it necessary to optimise plant
operations to achieve as much operational efficiency as possible. This paper presents two solutions to solve the optimisation problem: either the optimal predictive controller replaces the regulatory level PID controllers, or the predictive controller is implemented at the supervisory level. A comparison with popular multi-variable PID tuning methods demonstrates the superior performances of predictive control. The example is developed using a graphical predictive control software that uses the state of the art identification, control design optimisation and simulation LabVIEW toolkits for design verification and deployment. The control solutions can be easily imported to a real time platform for industrial applications.
LanguageEnglish
Publication statusPublished - 2006

Fingerprint

Model predictive control
Industrial applications
Controllers
Tuning
Economics

Keywords

  • predictive control design
  • predictive control
  • supervisory control

Cite this

Balbis, L., Kateb, R., & Ordys, A. W. (2006). Model predictive control design for industrial applications.
Balbis, L. ; Kateb, R. ; Ordys, A.W. / Model predictive control design for industrial applications.
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Balbis, L, Kateb, R & Ordys, AW 2006, 'Model predictive control design for industrial applications'.

Model predictive control design for industrial applications. / Balbis, L.; Kateb, R.; Ordys, A.W.

2006.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Model predictive control design for industrial applications

AU - Balbis, L.

AU - Kateb, R.

AU - Ordys, A.W.

PY - 2006

Y1 - 2006

N2 - In many industrial processes, the regulatory level based on PID controllers is able to maintain the process variables about the given set point values. However, economic reasons and operational constraints make it necessary to optimise plant operations to achieve as much operational efficiency as possible. This paper presents two solutions to solve the optimisation problem: either the optimal predictive controller replaces the regulatory level PID controllers, or the predictive controller is implemented at the supervisory level. A comparison with popular multi-variable PID tuning methods demonstrates the superior performances of predictive control. The example is developed using a graphical predictive control software that uses the state of the art identification, control design optimisation and simulation LabVIEW toolkits for design verification and deployment. The control solutions can be easily imported to a real time platform for industrial applications.

AB - In many industrial processes, the regulatory level based on PID controllers is able to maintain the process variables about the given set point values. However, economic reasons and operational constraints make it necessary to optimise plant operations to achieve as much operational efficiency as possible. This paper presents two solutions to solve the optimisation problem: either the optimal predictive controller replaces the regulatory level PID controllers, or the predictive controller is implemented at the supervisory level. A comparison with popular multi-variable PID tuning methods demonstrates the superior performances of predictive control. The example is developed using a graphical predictive control software that uses the state of the art identification, control design optimisation and simulation LabVIEW toolkits for design verification and deployment. The control solutions can be easily imported to a real time platform for industrial applications.

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KW - predictive control

KW - supervisory control

UR - http://ukacc.group.shef.ac.uk/Control_Conferences/Control2006/papers/f158.pdf

M3 - Paper

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