Adaptive control of a MEMS gyroscope using Lyapunov methods

Leland, R.P.

doi:10.1109/tcst.2005.860514

Cited by 126 publications

(63 citation statements)

References 11 publications

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“…1, Theorem 2.5.3 from [30] is applied. To prove that sufficient conditions (i)-(iii) are valid; the plant of estimation described in (15) is strictly proper, the reference input (i.e., s) to the plant is designed such that it is piecewise continuous and bounded, and s has been designed to be bounded, then from standard linear analysis tools, (15) can be used to show that s f ðtÞ; _ s f ðtÞ 2 L 1 . h…”

Section: Off-line Parameter Estimationmentioning

confidence: 99%

“…The diagnosis of actuator and sensor faults is investigated using a model-based approach, with observer-based residual generation. In [15], two Lyapunov-based adaptive controllers were proposed to compensate for uncertainties in the natural frequencies, mode coupling and damping, however some calibration (i.e., mass parameters of the gyro) was required. Recently, in [16][17][18], control algorithms were designed that reject imperfections (such as disturbances) in MEMS gyroscopes.…”

Section: Introductionmentioning

confidence: 99%

“…Their approach compensates for model inaccuracies under the assumption that the angular rate is constant. In fact, most of the relevant past works considered the angular rate to be constant with respect to time [2,5,10,15,[19][20][21] where the angular rate, indeed, is a time-varying signal. Furthermore, on-line estimation of the time-varying angular rate is one of the major control problems associated with MEMS gyroscopes.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2010

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a b s t r a c tIn this paper, a nonlinear estimation strategy for sensing the time-varying angular rate of a Z-axis MEMS gyroscope is presented. An off-line adaptive least-squares estimation strategy is first developed to accurately estimate the unknown model parameters. Both axes of a Z-axis MEMS gyroscope are then actively controlled utilizing an on-line controller/observer to facilitate time-varying angular rate sensing. The proposed nonlinear estimation strategy is developed based on a Lyapunov-based analysis, which proves that the time-varying angular rate experienced by the device can be estimated accurately. Two cases for angular rate are investigated which are time-varying and constant magnitudes. An adaptive controller/observer was also utilized for sensing the angular rate to investigate the performance of the proposed controller/observer. Representative numerical results are discussed to demonstrate the performance of the proposed nonlinear strategy in accurately sensing the applied angular rate. Overall, the proposed nonlinear controller/observer improves sensing the constant angular rate by 50% and the time-varying angular rate by 90% when compared with an adaptive controller/observer.

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Section: Off-line Parameter Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2010

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“…Nevertheless, the controller assumed an ideal model of the drive axis and did not consider the mechanical coupling terms between both axes. The research of the adaptive oscillation controller was extended in [11], where two vibrating axes were controlled to sense a constant rotation rate. Since the rotation rate is changing with time in reality, the adaptive controllers for measuring time-varying rates are developed in [12] and [13].…”

Section: Icro-electro-mechanical Systems (Mems)mentioning

confidence: 99%

Drive-Mode Control for Vibrational MEMS Gyroscopes

Dong

Avanesian

2009

IEEE Trans. Ind. Electron.

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“…In [9,10], one adaptive operation strategy is presented to control the MEMS -axis gyroscope, which offers a larger operational bandwidth, absence of zero-rate output, self-calibration, and large robustness to parameter variations caused by fabrication defects and ambient conditions. In [11,12], the sliding mode control is proposed to handle the vibrating proof mass, which achieves better estimation of the unknown angular velocity than conventional model reference adaptive feedback controller.…”

Section: Introductionmentioning

confidence: 99%

Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

Zhang

2017

Complexity

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This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learningparameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

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Adaptive control of a MEMS gyroscope using Lyapunov methods

Cited by 126 publications

References 11 publications

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

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

Drive-Mode Control for Vibrational MEMS Gyroscopes

Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

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