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On-line identification and control of pneumatic servo drives via a mixed-reality environment.

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Title: On-line identification and control of pneumatic servo drives via a mixed-reality environment.
Authors: Saleem, A.1 ashraf_saleem@yahoo.com, Abdrabbo, S.1 saberabdrabbo@yahoo.com, Tutunji, T.1 ttutunji@yahoo.com
Source: International Journal of Advanced Manufacturing Technology. Mar2009, Vol. 40 Issue 5/6, p518-530. 13p. 1 Color Photograph, 5 Diagrams, 2 Charts, 12 Graphs.
Subjects: Programming of numerically controlled machine tools, Algorithm research, Least squares, Box-Jenkins forecasting, Discrete-time systems
Abstract: This paper presents a method to identify and control electro-pneumatic servo drives in a real-time environment. Acquiring the system’s transfer function accurately can be difficult for nonlinear systems. This causes a great difficulty in servo-pneumatic system modeling and control. In order to avoid the complexity associated with nonlinear system modeling, a mixed-reality environment (MRE) is employed to identify the transfer function of the system using a recursive least squares (RLS) algorithm based on the auto-regressive moving-average (ARMA) model. On-line system identification can be conducted effectively and efficiently using the proposed method. The advantages of the proposed method include high accuracy in the identified system, low cost, and time reduction in tuning the controller parameters. Furthermore, the proposed method allows for on-line system control using different control schemes. The results obtained from the on-line experimental measured data are used to determine a discrete transfer function of the system. The best performance results are obtained using a fourth-order model with one-step prediction. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Advanced+Manufacturing+Technology%22">International Journal of Advanced Manufacturing Technology</searchLink>. Mar2009, Vol. 40 Issue 5/6, p518-530. 13p. 1 Color Photograph, 5 Diagrams, 2 Charts, 12 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Programming+of+numerically+controlled+machine+tools%22">Programming of numerically controlled machine tools</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithm+research%22">Algorithm research</searchLink><br /><searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink><br /><searchLink fieldCode="DE" term="%22Box-Jenkins+forecasting%22">Box-Jenkins forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete-time+systems%22">Discrete-time systems</searchLink>
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  Data: This paper presents a method to identify and control electro-pneumatic servo drives in a real-time environment. Acquiring the system’s transfer function accurately can be difficult for nonlinear systems. This causes a great difficulty in servo-pneumatic system modeling and control. In order to avoid the complexity associated with nonlinear system modeling, a mixed-reality environment (MRE) is employed to identify the transfer function of the system using a recursive least squares (RLS) algorithm based on the auto-regressive moving-average (ARMA) model. On-line system identification can be conducted effectively and efficiently using the proposed method. The advantages of the proposed method include high accuracy in the identified system, low cost, and time reduction in tuning the controller parameters. Furthermore, the proposed method allows for on-line system control using different control schemes. The results obtained from the on-line experimental measured data are used to determine a discrete transfer function of the system. The best performance results are obtained using a fourth-order model with one-step prediction. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00170-008-1374-z
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        Text: English
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      – SubjectFull: Algorithm research
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      – SubjectFull: Least squares
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      – SubjectFull: Box-Jenkins forecasting
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      – SubjectFull: Discrete-time systems
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              Text: Mar2009
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