Control Issues for MEMS

Bryzek, Janusz; Abbott, H. F.; Flannery, Anthony F.; Cagle, D.; Maitan, Jacek

doi:10.1109/cdc.2003.1273090

Cited by 42 publications

(31 citation statements)

References 10 publications

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“…The first MEMS devices were discrete open-loop pressure sensors developed in the 1960s [1]. The successful integration of MEMS and integrated circuits began in the 1980s, resulting in accelerometers, inkjet print heads, and gyro sensors as well as devices for HDTV displays and drug delivery systems.…”

Section: » a P P L I C A T I O N S O F C O N T R O L 18 Ieee Control mentioning

confidence: 99%

Control issues for microelectromechanical systems

Borovic¹,

Lewis²,

McCulley³

et al. 2006

IEEE Control Syst.

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Section: » a P P L I C A T I O N S O F C O N T R O L 18 Ieee Control mentioning

confidence: 99%

Control issues for microelectromechanical systems

Borovic¹,

Lewis²,

McCulley³

et al. 2006

IEEE Control Syst.

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“…is considered; the initial conditions (5) of model (3) can also be identified. The aim is to get a reliable model of the system with good predictive properties, suitable for example for control purposes such as in Zhu et al (2007); Liao et al (2004); Sane et al (2005); Daqaq et al (2006); Bryzek et al (2003).…”

Section: The Physical System Under Considerationmentioning

confidence: 99%

“…One of the main reasons which makes identification an important step when working with such micro-systems is that physical modelling in general does not permit to get very reliable models, useful for example for control (Zhu et al (2007); Liao et al (2004); Sane et al (2005); Daqaq et al (2006); Bryzek et al (2003); Vagia et al (2008)) or even dimensioning purposes. Indeed, due to the very small size of these systems, many parameter values cannot be directly measured and dynamic underlying phenomena are difficult to correctly be described from the only physical analysis; in this case, identification process can be the only way to get reliable models.…”

Section: Introductionmentioning

confidence: 99%

Identification of nonlinear dynamic models of electrostatically actuated MEMS

Casenave

Montseny

Camon

2010

Control Engineering Practice

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This paper focuses on the identification of nonlinear dynamic models for physical systems such as electrostatically actuated micro-electro-mechanical systems (MEMS). The proposed approach consists in transforming, by means of suitable global operations, the input-output differential model in such a way that the new equivalent formulation is well adapted to the identification problem, thanks to the following properties: first, the linearity with respect to the parameters to be identified is preserved, second, the continuous dependence on noise measurements is restored. Consequently, a simple least-square resolution can be used, in such a way that some of the difficulties classically encountered with identification methods are by-passed. The method is implemented on real measurement data from a physical system.

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“…Due to their very small size, many parameters of those systems cannot be accurately known; in this case, identification process can be the only way to get reliable models, useful for example for control (Zhu (2007); Liao (2004); Sane (2005); Daqaq (2006); Bryzek (2003)) or even dimensioning purposes. Several informations and techniques about identification of dynamic systems will be found in Ljung (1999) and Garnier (2004).…”

Section: Introductionmentioning

confidence: 99%

Identification of Electrostatically Actuated MEMS Models from Real Measurement Data

Casenave

Montseny

Camon

2009

IFAC Proceedings Volumes

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This paper focuses on the identification of nonlinear dynamic models for physical systems such as Micro-Electro-Mechanical Systems (MEMS) from measurement data. The proposed approach consists in transforming, by means of suitable global operations, the specific input-output differential physical model of the system elaborated from physical analysis, in such a way that we get a new equivalent model formulation specifically adapted to the identification problem. Thanks to the equivalence of the dynamic model and the derived identification problem, the so-identified model remains of continuous-time type, with a clear physical meaning of any of its components, which is not the case when using, for example, black-boxes approaches. The method is implemented on real measurement data from a physical system.

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Control Issues for MEMS

Cited by 42 publications

References 10 publications

Control issues for microelectromechanical systems

Control issues for microelectromechanical systems

Identification of nonlinear dynamic models of electrostatically actuated MEMS

Identification of Electrostatically Actuated MEMS Models from Real Measurement Data

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