System Identification of a MEMS Gyroscope

M’Closkey, Robert T.; Gibson, Steve; Hui, Jason K.

doi:10.1115/1.1369360

Cited by 21 publications

(19 citation statements)

References 8 publications

(10 reference statements)

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“…Previous work on system identification has demonstrated a systematic approach to identifying anisoelasticity of a torsional "rocking" MEMS rate gyroscope using model synthesis and Markov parameters [16]. However, in linear mass gyroscopes, there are certain characteristics of dynamic motion that allow for a simpler algorithm for identifying anisoelasticities.…”

Section: Identification Of Errorsmentioning

confidence: 99%

“…We will assume that and have zero mean (centered about the origin) and that we have experimentally acquired covariances between and . A covariance matrix can be expressed by (16) where and are the variances of and and the covariance between and is given by (17) with the index of summation going over the entire sample size . The covariance matrix is a numerical measure of the coupling between variables and in the case when is diagonal, the vectors of are uncorrelated, i.e., the position has no influence on the position.…”

Section: A Algorithmmentioning

confidence: 99%

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Active structural error suppression in MEMS vibratory rate integrating gyroscopes

2003

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Due to restrictive tolerancing in microfabrication, structural imperfections that reduce performance of fabricated micro devices are typical. In microelectromechanical vibratory gyroscopes, feedback control is a common strategy in attempting to correct the imperfections. However, a purely feedback control can be insufficient for compensation of all the errors, requiring post processing in the form of laser trimming to achieve higher levels of performance. In this paper, we explore another alternative: the design and implementation of a dual stage control architecture with self-calibration and feedback capabilities. The self-calibrative portion of the control identifies and electronically "trims" large imperfections, while the feedback control compensates for remaining small nonidealities and in-operation perturbations. Presented here is an algorithm for in-situ imperfection identification based on the dynamic response of the device. A realization of the dual stage control architecture is proposed for a gyroscope using nonlinear electrostatic parallel plate actuators. Modeling and simulation results which demonstrate successful compensation of imperfections with the proposed architecture for a device with 10% fabrication error appearing in the form of stiffness nonidealities and subjected to further 1% in-run perturbations are presented.Index Terms-Error suppression, microelectromechanical systems (MEMS), rate integrating gyroscopes, smart MEMS.

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Section: Identification Of Errorsmentioning

confidence: 99%

Section: A Algorithmmentioning

confidence: 99%

Active structural error suppression in MEMS vibratory rate integrating gyroscopes

2003

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“…Recent R&D work, documenting examples of these next-generation MEMS based system-level building blocks can be found in references [1][2][3][4]. Shkel [1] has outlined an architecture with functionality that includes calibration, testing, and compensation.…”

Section: Introductionmentioning

confidence: 99%

“…M'Closkey et.al. [3] have carried this methodology further by performing on-line system parameter identification to enable gyro angular rate measurement error correction dynamically, using a chirp signal as a wide-band excitation source. We too have independently found that a chirp excitation source provides a more robust excitation source, when compared to sinusoidal and square-wave excitation that is typically used.…”

Section: Introductionmentioning

confidence: 99%

<title>Adaptive estimation of angular velocity and acceleration of a single-axis MEMS coriolis sensor</title>

Karmarkar

Singh

2002

SPIE Proceedings

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MEMS fabrication technology has facilitated the implementation of a broad variety of low-cost miniature sensors, including those for measuring linear and angular rates of objects-of-interest. Earlier work has focused on component-level sensor fabrication issues and proof-of-concept verification of their sensing ability. fhi prior work has provided the foundation for design and performance assessment of the next generation of multi-axis embedded MEMS sensors at a subsystem level. Subsequently, this will lead to the fabrication of äifl integrated self-calibrating 6 degree-of-freedom (DOF) strapdown inertial sensor assembled in a low-cost miniature package.This in turn will lead to a variety of applications that are currently unrealistic because of cost-weight-power considerations. This particular effort is directed toward establishing the feasibility of extracting additional information from a MEMS sensor by appropriately exciting a single-axis Coriolis sensor, for example, to generate optimum angular velocity and angular acceleration estimates, whereas prior studies have shown only the ability to generate approximate angular rotational velocity measurements. This work entailed the dynamic modeling of a representative MEMS sensor and several different angular velocity and angular acceleration driving functions in a MATLAB-based simulation. The corresponding "raw" sensor outputs were then optimally processed to concurrently generate estimates of both angular velocity and angular acceleration. The graphical results from these simulation studies are included to show the benefit of physically co-locating a digital computing element with the MEMS sensor, thereby facilitating the creation of a new generation of digital "smart sensors" , that will be capable of self-calibration based performance deterioration assessment, fault detection and recovery.

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“…We have employed an ARX modeling scheme [6] in the past to estimate various sensor parameters, however, we have observed that the standard deviation of ∆ω is approximately 0.1 Hz as shown in Fig. 2, which is on the order of the resolution we require to obtain the full benefits of tuning (in order to make a fair comparison, both the ARX models and frequency domain models discussed in the sequel use approximately the same amount of data with the input power concentrated in a narrow band about the modes of interest).…”

Section: B Generation Of Frequency Response Datamentioning

confidence: 99%

Real-time tuning of MEMS gyro dynamics

Kim

M’Closkey

Proceedings of the 2005, American Control Conference, 2005.

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This paper reports real-time tuning of the JPLBoeing micromachined vibratory rate sensor. The ideal sensor is designed to operate in a degenerate condition in which two modes of vibration have equal resonant frequencies. This condition achieves the best possible signal-to-noise ratio thereby maximizing sensor performance. A frequency split between the two modes, however, is inevitable in actual devices and leads to degraded performance. To modify the sensor dynamics to a desired condition, we have studied the bias potential effect on the sensor dynamics and successfully implemented a real-time tuning process via electrostatic forces to reduce the frequency split to less than 0.1 Hz when the nominal modal frequencies are near 4.4 kHz. A closed-loop identification method is employed for rapid and precise empirical frequency response estimates of the sensor dynamics. An LMI-based parameter estimation scheme produces an excellent fit of the model to the frequency response data and this enables the successful implementation of a steepest descent algorithm. Transformations for decoupling the MIMO sensor dynamics are also motivated and demonstrated.

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System Identification of a MEMS Gyroscope

Cited by 21 publications

References 8 publications

Active structural error suppression in MEMS vibratory rate integrating gyroscopes

Active structural error suppression in MEMS vibratory rate integrating gyroscopes

<title>Adaptive estimation of angular velocity and acceleration of a single-axis MEMS coriolis sensor</title>

Real-time tuning of MEMS gyro dynamics

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