Abstract
In this research, the input/output data of a MIMO nonlinear system are used to create intelligent models for nonlinear systems. Multi layer perceptrons and neuro-fuzzy networks are utilized for the intelligent models. To make these models suitable for the predictive control, a variety of subtle points should be considered. Recurrent models and subtractive clustering are used in this research, and a pre-processing is applied to the columns of the raw data. Then the prepared data are used to train models. A reliable checking process is also studied. A Catalytic Continuous Stirred Tank Reactor is used as a case study. A computer model is used to gather the input data rather than a real one. Finally, the simulation is successfully performed to indicate the capabilities of the intelligent modeling method as well as the importance of the design considerations offered in this paper.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC |
| Volume | 17 |
| Edition | 1 PART 1 |
| DOIs | |
| Publication status | Published - 2008 |
| Event | 17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of Duration: Jul 6 2008 → Jul 11 2008 |
Other
| Other | 17th World Congress, International Federation of Automatic Control, IFAC |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 7/6/08 → 7/11/08 |
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Keywords
- Identification for control
- Iterative modelling and control design
- Nonlinear system identification
ASJC Scopus subject areas
- Control and Systems Engineering
Cite this
Intelligent modelling of MIMO nonlinear dynamic process plants for predictive control purposes. / Mohammadzaheri, Morteza; Chen, Lei.
Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC. Vol. 17 1 PART 1. ed. 2008.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Intelligent modelling of MIMO nonlinear dynamic process plants for predictive control purposes
AU - Mohammadzaheri, Morteza
AU - Chen, Lei
PY - 2008
Y1 - 2008
N2 - In this research, the input/output data of a MIMO nonlinear system are used to create intelligent models for nonlinear systems. Multi layer perceptrons and neuro-fuzzy networks are utilized for the intelligent models. To make these models suitable for the predictive control, a variety of subtle points should be considered. Recurrent models and subtractive clustering are used in this research, and a pre-processing is applied to the columns of the raw data. Then the prepared data are used to train models. A reliable checking process is also studied. A Catalytic Continuous Stirred Tank Reactor is used as a case study. A computer model is used to gather the input data rather than a real one. Finally, the simulation is successfully performed to indicate the capabilities of the intelligent modeling method as well as the importance of the design considerations offered in this paper.
AB - In this research, the input/output data of a MIMO nonlinear system are used to create intelligent models for nonlinear systems. Multi layer perceptrons and neuro-fuzzy networks are utilized for the intelligent models. To make these models suitable for the predictive control, a variety of subtle points should be considered. Recurrent models and subtractive clustering are used in this research, and a pre-processing is applied to the columns of the raw data. Then the prepared data are used to train models. A reliable checking process is also studied. A Catalytic Continuous Stirred Tank Reactor is used as a case study. A computer model is used to gather the input data rather than a real one. Finally, the simulation is successfully performed to indicate the capabilities of the intelligent modeling method as well as the importance of the design considerations offered in this paper.
KW - Identification for control
KW - Iterative modelling and control design
KW - Nonlinear system identification
UR - http://www.scopus.com/inward/record.url?scp=79961019759&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79961019759&partnerID=8YFLogxK
U2 - 10.3182/20080706-5-KR-1001.0978
DO - 10.3182/20080706-5-KR-1001.0978
M3 - Conference contribution
AN - SCOPUS:79961019759
SN - 9783902661005
VL - 17
BT - Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
ER -