Data-Driven control design by prediction error identification for a refrigeration system based on vapor compression

Daniel D. Huff; Gustavo R. Gonçalves da Silva; Lucíola Campestrini

doi:10.1016/j.ifacol.2018.06.186

Data-Driven control design by prediction error identification for a refrigeration system based on vapor compression

Daniel D. Huff (Corresponding author), Gustavo R. Gonçalves da Silva, Lucíola Campestrini

Research output: Contribution to journal › Conference article › peer-review

5 Citations (Scopus)

Abstract

This paper deals with data-driven control design in a Model Reference (MR) framework for multivariable systems. Based on a batch of input-output data collected on the process, a fixed structure controller is estimated without using a process model, by embedding the control design problem in the Prediction Error (PE) identification of an optimal controller. A multivariable extension of the OCI (Optimal Controller Identification) method is applied in the design of PID controllers for a refrigeration system based on vapor compression, which is the subject of the benchmark process challenge of the IFAC PID 2018 conference. Simulation results show the obtained controllers perform significantly better than the ones provided by the benchmark challenge.

Original language	English
Pages (from-to)	704-709
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	51
Issue number	4
DOIs	https://doi.org/10.1016/j.ifacol.2018.06.186
Publication status	Published - 2018
Externally published	Yes
Event	3rd IFAC Conference on Advances in Proportional-Integral-Derivative Control PID 2018 - Ghent, Belgium Duration: 9 May 2018 → 11 May 2018

Keywords

Data-driven control
MIMO systems
Model reference
OCI
refrigeration system

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10.1016/j.ifacol.2018.06.186

Cite this

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title = "Data-Driven control design by prediction error identification for a refrigeration system based on vapor compression",

abstract = "This paper deals with data-driven control design in a Model Reference (MR) framework for multivariable systems. Based on a batch of input-output data collected on the process, a fixed structure controller is estimated without using a process model, by embedding the control design problem in the Prediction Error (PE) identification of an optimal controller. A multivariable extension of the OCI (Optimal Controller Identification) method is applied in the design of PID controllers for a refrigeration system based on vapor compression, which is the subject of the benchmark process challenge of the IFAC PID 2018 conference. Simulation results show the obtained controllers perform significantly better than the ones provided by the benchmark challenge.",

keywords = "Data-driven control, MIMO systems, Model reference, OCI, refrigeration system",

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AU - Huff, Daniel D.

AU - Gonçalves da Silva, Gustavo R.

AU - Campestrini, Lucíola

PY - 2018

Y1 - 2018

N2 - This paper deals with data-driven control design in a Model Reference (MR) framework for multivariable systems. Based on a batch of input-output data collected on the process, a fixed structure controller is estimated without using a process model, by embedding the control design problem in the Prediction Error (PE) identification of an optimal controller. A multivariable extension of the OCI (Optimal Controller Identification) method is applied in the design of PID controllers for a refrigeration system based on vapor compression, which is the subject of the benchmark process challenge of the IFAC PID 2018 conference. Simulation results show the obtained controllers perform significantly better than the ones provided by the benchmark challenge.

AB - This paper deals with data-driven control design in a Model Reference (MR) framework for multivariable systems. Based on a batch of input-output data collected on the process, a fixed structure controller is estimated without using a process model, by embedding the control design problem in the Prediction Error (PE) identification of an optimal controller. A multivariable extension of the OCI (Optimal Controller Identification) method is applied in the design of PID controllers for a refrigeration system based on vapor compression, which is the subject of the benchmark process challenge of the IFAC PID 2018 conference. Simulation results show the obtained controllers perform significantly better than the ones provided by the benchmark challenge.

KW - Data-driven control

KW - MIMO systems

KW - Model reference

KW - OCI

KW - refrigeration system

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Data-Driven control design by prediction error identification for a refrigeration system based on vapor compression

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Keywords

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