Journal of Micro-Bio Robotics

, Volume 15, Issue 1, pp 53–64 | Cite as

Modeling and experimental characterization of an active MEMS based force sensor

  • Jonathan CailliezEmail author
  • Mokrane Boudaoud
  • Abdenbi Mohand-Ousaid
  • Antoine Weill–Duflos
  • Sinan Haliyo
  • Stéphane Régnier
Research article


Active force sensors are based on the principle of force balancing using a feedback control. They allow, unlike passive sensors, the static characterization of forces without interference of the sensor mechanical properties on the estimated stiffness of the object to be studied. This capability is fundamental when dealing with the mechanical characterization of samples having a wide range of stiffness. This paper deals with the modeling and the experimental characterization of a new active MEMS based force sensor. This sensor includes folded-flexure type suspensions and a differential comb drive actuation allowing a linear force/voltage relationship. A control oriented electromechanical model is proposed and validated experimentally in static and dynamic operating modes using a stroboscopic measurement system. This work is a first step towards new MEMS active force sensor with high resonant frequency (>2kHz) and high linear measurement force range (50 μN). The advantage of this structure is to be able to change the sensor operating point without changing the sensor dynamics. Thus simplifying the control law. Modifying the operating point allows performing an accurate self positioning of the probe in close proximity to the surface to be studied.


Force sensor MEMS Identification 



This work has been partially sponsored by the French National Research Agency project MultiFlag (Grant No. ANR-16-CE33-0019) and COLAMIR (Grant No. ANR-16-CE10-0009) and the project Robotex Equipment of Excellence (ANR-10-EQPX-44). The MEMS were realized thanks to the help of the RENATECH platform and the IEMN lab (Cité Scientifique – Avenue Poincaré BP 60069, 59652 Villeneuve d’Ascq Cedex).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Sorbonne Université, campus Pierre et Marie Curie/ CNRS UMR 7222ParisFrance
  2. 2.FEMTO-ST Institute, AS2M DepartmentUniversity Bourgogne Franche-Comte / UFC / CNRS UMR-6174/ENSMMBesançonFrance
  3. 3.McGill University, Centre for Intelligent MachinesMontréalCanada

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