Improved intelligent identification of uncertainty bounds: design, model validation and stability analysis

Akmeliawati, Rini and Raafat, Safanah and Martono, Wahyudi (2012) Improved intelligent identification of uncertainty bounds: design, model validation and stability analysis. International Journal of Modelling, Identification and Control, 15 (3). pp. 173-184. ISSN 1746-6172 E-ISSN 1746-6180

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Abstract

Identification of uncertainty bounds in robust control design is known to be a critical issue that attracts the attention of research in robust control field recently. Nevertheless, the practical implementation involves a trial and error procedure, which depends on the designer prior knowledge and the available information about the system under study. Artificial intelligent techniques provide a suitable solution to such a problem. In this paper a new intelligent identification method of uncertainty bound utilises an adaptive neuro-fuzzy inference system (ANFIS) in an enhanced feedback scheme is proposed. The proposed ANFIS structure enables accurate determination of the uncertainty bounds and guarantees robust stability and performance. In our proposed technique, the validation of the intelligent identified uncertainty weighting function is based on the measurement of both the v-gap metric and the stability margin that result from the corresponding robust controller design. Additionally, these two indices are used to improve the accuracy of the intelligent estimation of uncertainty bound in conjunction with the robust control design requirements. The enhanced intelligent identification of uncertainty bound is demonstrated on a servo positioning system. Simulation and experimental results proves the validity of the applied approach; more reliable and highly efficient estimation of the uncertainty weighting function for robust controller design.

Item Type:	Article (Journal)
Additional Information:	5806/28159
Uncontrolled Keywords:	adaptive neuro-fuzzy inference system; ANFIS; H∞ robust controller; identification; Servo positioning system; uncertainty bound; v-gap metric; model validation.
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ212 Control engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Prof. Dr. Rini Akmeliawati
Date Deposited:	04 Jan 2013 10:13
Last Modified:	21 Jun 2017 14:12
URI:	http://irep.iium.edu.my/id/eprint/28159

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