Chapter

Evolvable Hardware

Part of the series Genetic and Evolutionary Computation pp 209-222

Hardware Platforms for Electrostatic Tuning of Mems Gyroscope Using Nature-Inspired Computation

  • Didier KeymeulenAffiliated withJet Propulsion Laboratory
  • , Michael I. FergusonAffiliated withJet Propulsion Laboratory
  • , Luke BreuerAffiliated withJet Propulsion Laboratory
  • , Wolfgang FinkAffiliated withJet Propulsion Laboratory
  • , Boris OksAffiliated withJet Propulsion Laboratory
  • , Chris PeayAffiliated withJet Propulsion Laboratory
  • , Richard TerrileAffiliated withJet Propulsion Laboratory
  • , Yen-Cheng Affiliated withMechanical and Aerospace Engineering Department, University of California
  • , Dennis KimAffiliated withMechanical and Aerospace Engineering Department, University of California
    • , Eric MacDonaldAffiliated withUniversity of Texas at El Paso
    • , David FoorAffiliated withTexas A&M University-Kingsville

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Abstract

We propose a tuning method for Micro-Electro-Mechanical Systems (MEMS) gyroscopes based on evolutionary computation to increase the accuracy of MEMS gyroscopes through electrostatic tuning. The tuning method was tested for the second generation JPL/Boeing Post-resonator MEMS gyroscope using the measurement of the frequency response of the MEMS device in open-loop operation. We also report on the development and preliminary results of a hardware platform for integrated tuning based on “switched drive-angle” of MEMS gyroscopes whereby the same gyro is operated with its drive direction first at 0° and then at 90°. The control of this device is implemented through a digital design on a Field Programmable Gate Array (FPGA). The hardware platform easily transitions to an embedded solution that allows for the miniaturization of the system to a single chip.

Key words

MEMS tuning genetic algorithm simulated annealing FPGA