Comparative Study of System Identification Approaches for Adaptive Tracking of Mems Gyroscope

Fei, Juntao; Yang, Yuzheng

doi:10.2316/journal.206.2012.3.206-3693

Cited by 8 publications

(9 citation statements)

References 19 publications

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“…(7) and an adaptive law Eq. (18) for adjusting the parameters vector θ(t) are designed to satisfy that the closed-loop system must be globally stable in the sense that all the variables, q(t), θ(t), and u must be uniformly stable and the tracking error e(t) should be as small as possible.…”

Section: Design and Stability Analysis Of Adaptive Fuzzy Controllermentioning

confidence: 99%

“…The comparative studies between the adaptive fuzzy controller with the supervisory compensator and the adaptive controller in ref. [18] are implemented using the same simulation parameters such as external disturbances, model parameters, initial states values etc. It can be observed that the desired trajectory can be tracked more quickly and the tracking error can converge to zero in a shorter time using the adaptive fuzzy controller with the supervisory compensator than the standard adaptive controller in the presence of the same external disturbances.…”

Section: Simulation Studymentioning

confidence: 99%

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Robust adaptive fuzzy controller with supervisory compensator for MEMS gyroscope sensor

2015

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SUMMARYIn this paper, a robust adaptive fuzzy controller is proposed to improve the robustness and position tracking of a MEMS gyroscope sensor. The proposed controller is designed as an indirect adaptive fuzzy controller with a supervisory compensator. It incorporates a fuzzy inference system with an adaptive controller in a unified Lyapunov framework, which can approximate and compensate for the unknown system dynamics and nonlinearities in the MEMS gyroscope. The parameters of the membership functions in the fuzzy controller can be adjusted online based on the Lyapunov method. Moreover, a supervisory controller is employed to guarantee the asymptotic stability of the closed-loop system and boundedness of the state variables in the MEMS gyroscope model. Numerical simulations demonstrate the proposed robust adaptive fuzzy controller has satisfactory tracking performance and robustness in the presence of external disturbances.

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Section: Design and Stability Analysis Of Adaptive Fuzzy Controllermentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

Robust adaptive fuzzy controller with supervisory compensator for MEMS gyroscope sensor

2015

Self Cite

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“…The captured data, related to several test sets at different conditions, were used for model estimation and validation purposes. In [3], a comparative study of adaptive vibration control approaches is presented for the system identification for microelectro-mechanical systems (MEMS) z-axis gyroscope. In [4] and [5], the structure and operation of inertial stabilized platforms were studied.…”

Section: Introductionmentioning

confidence: 99%

A laboratory method for obtaining two degrees of freedom gyro-scopic stabilizer transfer function

Darestani¹,

Moussavi²,

Amiri³

2016

IJET

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Obtaining transfer function of electrical, mechanical, etc. systems can provide this possibility for the researchers to investigate the behaviours of desired systems based on different inputs in various working circumstances without need to laboratory equipment which it results in lower consumption of time and expense. The aim of current research is obtaining the existing gyroscopic stabilizer transfer function. The way we used in this article is the newest laboratory way for obtaining transfer function of gyroscopic stabilizers. This aim is achieved by using laboratory equipment such as a two degrees of freedom gyro stabilized platform that an imaging system is installed on it as the load, target simulator table with one degree of freedom, and electronic conversion board of RS488 to RS232 serial communication standard, etc. An input which excites all modes (search and track) of two degrees of freedom gyro stabilized platform is introduced to under test system and the system behaviour toward the introduced input is saved and finally the transfer function of existing two degrees of freedom gyro stabilized platform is obtained using system identification toolbox in MATLAB. At the end of this article, the step response of transfer function obtained through the desired experiment in the laboratory compared with the step response obtained through simulations that a %10 difference between them is observed.

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“…[4], Wang et al developed the nonlinear dynamic model of a gyroscope system and used the SMC to guarantee the stability of the whole system. Fei et al 5 incorporated the SMC strategy into adaptive control. Ebrahimi et al 6 invoked a terminal SMC scheme to develop tracking control of the drive and sense modes.…”

Section: Introductionmentioning

confidence: 99%

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

Fei

Yang

2014

Robotica

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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.

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Comparative Study of System Identification Approaches for Adaptive Tracking of Mems Gyroscope

Cited by 8 publications

References 19 publications

Robust adaptive fuzzy controller with supervisory compensator for MEMS gyroscope sensor

Robust adaptive fuzzy controller with supervisory compensator for MEMS gyroscope sensor

A laboratory method for obtaining two degrees of freedom gyro-scopic stabilizer transfer function

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

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