Sensing of the time-varying angular rate for MEMS Z-axis gyroscopes

Salah, Mohammad; McIntyre, Michael L.; Dawson, D.M.; Wagner, John R.; Tatlıcıoğlu, Enver

doi:10.1016/j.mechatronics.2010.07.001

Cited by 12 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MEMS gyroscope errors contain system errors and random errors. System errors such as bias and scale are compensated by laboratory turntable calibration [ 12 ] and specific movement state-based system-level calibration. However, random errors have characteristics of uncertainty and randomness [ 13 ], which cannot be fully compensated by analysis methods, yet can be restrained with a filtering method [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Filtering Approach Based on the Dynamic Variance Model for Reducing MEMS Gyroscope Random Error

Zhang

Peng

Mou

et al. 2018

Sensors

View full text Add to dashboard Cite

To improve the dynamic random error compensation accuracy of the Micro Electro Mechanical System (MEMS) gyroscope at different angular rates, an adaptive filtering approach based on the dynamic variance model was proposed. In this paper, experimental data were utilized to fit the dynamic variance model which describes the nonlinear mapping relations between the MEMS gyroscope output data variance and the input angular rate. After that, the dynamic variance model was applied to online adjustment of the Kalman Filter measurement noise coefficients. The proposed approach suppressed the interference from the angular rate in the filtering results. Dynamic random errors were better estimated and reduced. Turntable experiment results indicated that the adaptive filtering approach compensated for the MEMS gyroscope dynamic random error effectively both in the constant angular rate condition and the continuous changing angular rate condition, thus achieving adaptive dynamic random error compensation.

show abstract

Section: Introductionmentioning

confidence: 99%

An Adaptive Filtering Approach Based on the Dynamic Variance Model for Reducing MEMS Gyroscope Random Error

Zhang

Peng

Mou

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…In the last few years, some control approaches have been proposed to control the MEMS vibratory rate gyroscope (Leland, 2003;Park and Horowitz, 2003;Oboe et al, 2005;Salah et al, 2010). The MEMS gyroscope have nonlinear dynamics with uncertainties and disturbances and therefore the conventional control approaches based on linearized model do not give satisfactory control performance.…”

Section: Introductionmentioning

confidence: 99%

Design of an Adaptive Fuzzy Sliding Mode Control Using Supervisory Fuzzy Control for Micro-Electro-Mechanical Systems (MEMS) Z-Axis Gyroscope

Moghanni-Bavil-Olyaei

Gholami

2014

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

This paper presents a novel robust adaptive control strategy for MEMS gyroscope, based on the coupling of the fuzzy logic control with sliding mode control (SMC) approach and adaptive laws. The drawbacks of the conventional SMC include chattering phenomenon and requirement of a priori knowledge of the bounds of uncertainties. In this paper, these problems are suitably attenuated by adopting an adaptive fuzzy sliding mode control (AFSMC) approach which uses two additional fuzzy self-tuning controllers as supervisory fuzzy systems to adaptively tune the output control gain, sliding gain of the AFSMC in order to speed up the convergence rates of the tracking errors and parameter estimations with a desired level of attenuation. Control system stability is proved by Lyapunov method. Numerical simulations using the MEMS gyroscope model with uncertainties demonstrate the effectiveness of this approach in high speed trajectory tracking problems and robustness in estimating the gyroscope parameters and the angular velocity. Numerical simulations show that the chattering is effectively attenuated by this method. Convergence rate of tracking errors and the gyroscope parameters is better than convergence rates in the literature.

show abstract

“…Leland 2 presented controllers to tune the drive axis oscillation to a fixed frequency and amplitude, and these drive the sense axis vibration to zero for force-to-rebalance operation. Salah et al 3 developed a new control strategy to sense the time-varying angular rate for MEMS z-axis gyroscopes. Generally, the adaptive controllers presented earlier are based on the assumption that structure of the system model is known with unknown system parameters.…”

Section: Introductionmentioning

confidence: 99%

Robust neural network control of MEMS gyroscope using adaptive sliding mode compensator

Fei

Yang

2014

Robotica

View full text Add to dashboard Cite

SUMMARYA new robust neural sliding mode (RNSM) tracking control scheme using radial basis function (RBF) neural network (NN) is presented for MEMS z-axis gyroscope to achieve robustness and asymptotic tracking error convergence. An adaptive RBF NN controller is developed to approximate and compensate the large uncertain system dynamics, and a robust compensator is designed to eliminate the impact of NN modeling error and external disturbances for guaranteeing the asymptotic stability property. Moreover, another RBF NN is employed to learn the upper bound of NN modeling error and external disturbances, so the prior knowledge of the upper bound of system uncertainties is not required. All the adaptive laws in the RNSM control system are derived in the same Lyapunov framework, which can guarantee the stability of the closed loop system. Comparative numerical simulations for an MEMS gyroscope are investigated to verify the effectiveness of the proposed RNSM tracking control scheme.

show abstract

Sensing of the time-varying angular rate for MEMS Z-axis gyroscopes

Cited by 12 publications

References 28 publications

An Adaptive Filtering Approach Based on the Dynamic Variance Model for Reducing MEMS Gyroscope Random Error

An Adaptive Filtering Approach Based on the Dynamic Variance Model for Reducing MEMS Gyroscope Random Error

Design of an Adaptive Fuzzy Sliding Mode Control Using Supervisory Fuzzy Control for Micro-Electro-Mechanical Systems (MEMS) Z-Axis Gyroscope

Robust neural network control of MEMS gyroscope using adaptive sliding mode compensator

Contact Info

Product

Resources

About