Sliding mode control of a simulated MEMS gyroscope

Batur, C.; Sreeramreddy, T.; Khasawneh, Qais A.

doi:10.1016/s0019-0578(07)60069-x

Cited by 97 publications

(36 citation statements)

References 5 publications

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“…Nevertheless, applying controllers to MEMS-based voltage reference sources is necessary because it has great impact on increasing the long term stability and accuracy of reference voltage. It is not blowing it out of proportion that applying various control algorithms to MEMS devices to yield error subside and a better performance is a nonnegligible problematic issue [9][10][11][12][13][14]. In addition, adaptive and robust control methods lie in the center of meticulousness and challenge due to deficiency in modeling of MEMS devices and the parametric uncertainties in fabrication procedures [15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control of MEMS AC Voltage Reference Source

Ranjbar

Mehrnezhad

Suratgar

2017

Journal of Control Science and Engineering

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The accuracy of physical parameters of a tunable MEMS capacitor, as the major part of MEMS AC voltage reference, is of great importance to achieve an accurate output voltage free of the malfunctioning noise and disturbance. Even though strenuous endeavors are made to fabricate MEMS tunable capacitors with desiderated accurate physical characteristics and ameliorate exactness of physical parameters' values, parametric uncertainties ineluctably emerge in fabrication process attributable to imperfections in micromachining process. First off, this paper considers applying an adaptive sliding mode controller design in the MEMS AC voltage reference source so that it is capable of giving off a well-regulated output voltage in defiance of jumbling parametric uncertainties in the plant dynamics and also aggravating external disturbance imposed on the system. Secondly, it puts an investigatory comparison with the designed model reference adaptive controller and the pole-placement state feedback one into one's prospective. Not only does the tuned adaptive sliding mode controller show remarkable robustness against slow parameter variation and external disturbance being compared to the pole-placement state feedback one, but also it immensely gets robust against the external disturbance in comparison with the conventional adaptive controller. The simulation results are promising.

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Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control of MEMS AC Voltage Reference Source

Ranjbar

Mehrnezhad

Suratgar

2017

Journal of Control Science and Engineering

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“…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. Since then, in the presence of significant uncertainties, the regulated model-based and non-modelbased sliding model control approaches are presented to improve tracking control of the drive and sense modes of the vibratory gyroscope in [13].…”

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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“…Micro electro-mechanical systems (MEMS) gyroscope, which is one of the micro-machined inertial sensors, and commonly used to measure the angular velocity in many areas including platform stabilization in space applications, activity monitoring in biomedical applications, sport equipment in consumer applications, robotics and machine and vibration monitoring in industrial applications, monitoring mechanical shock and vibration during transportation in automotive applications (Batur, Sreeramreddy, & Khasawneh, 2006;Fei & Batur, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Batur et al (2006) developed a sliding-mode control for an MEMS gyroscope system combined with a force balancing control strategy to identify angular velocity. Fei and Batur (2009) derived an adaptive sliding-mode control (ASMC) with Proportional-Integral sliding surface for an MEMS gyroscope in order to enable the system to track the desired trajectory or reference model and to identify its angular velocity, but chattering phenomena still was a problem.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy terminal sliding-mode control of MEMSz-axis gyroscope with extended Kalman filter observer

Gholami

Moghanni-Bavil-Olyaei

2014

Systems Science & Control Engineering

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This paper presents a new terminal sliding-mode control (TSMC) for the micro electro-mechanical systems (MEMS) z-axis gyroscope. However, TSMC may chatter when uncertainty values are overestimated or may exhibit a steady-state error when uncertainty values are underestimated. In this paper, an adaptive fuzzy terminal sliding-mode controller is designed to retain the advantages of the terminal sliding-mode controller and to reduce the chattering occurred with the terminal sliding-mode controller. Stability analysis of the TSMC is presented in the presence of external disturbance and model uncertainties. Moreover, an extended Kalman filter (EKF) observer is designed to estimate the angular velocity and all of the gyroscope parameters and convergence analysis of the proposed EKF algorithm is presented. Numerical simulations using the nonlinear dynamic model of an MEMS z-axis gyroscope with uncertainties demonstrate the effectiveness of the approach in fast trajectory tracking problems and robustness in estimating the gyroscope parameters and also the angular velocity.

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Sliding mode control of a simulated MEMS gyroscope

Cited by 97 publications

References 5 publications

Adaptive Sliding Mode Control of MEMS AC Voltage Reference Source

Adaptive Sliding Mode Control of MEMS AC Voltage Reference Source

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

Adaptive fuzzy terminal sliding-mode control of MEMSz-axis gyroscope with extended Kalman filter observer

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