The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform

Juntao Fei - One of the best experts on this subject based on the ideXlab platform.

  • Backstepping control of MEMS Gyroscope using adaptive neural observer
    International Journal of Machine Learning and Cybernetics, 2016
    Co-Authors: Cheng Lu, Juntao Fei
    Abstract:

    In this paper, a backstepping controller with an adaptive neural states observer is proposed for MEMS (Micro-Electro-Merchanical-System) Gyroscopes in the presence of model uncertainties and external disturbance. Gyroscope states are usually assumed to be available in controller design procedure. However, Gyroscope states may be unavailable in some circumstances. In this paper, an adaptive neural states observer is employed to estimate Gyroscope states without physical sensors and thus can help reducing complexity of the Gyroscope system. A backstepping controller is utilized to control the vibrating amplitude and frequency of the mass proof and the control law is carried out with states estimation rather than actual Gyroscope states. Adaptive laws are investigated in the Lyapunov stability framework to guarantee the stability of the observer. Numerical simulation results demonstrate the effectiveness of the proposed control scheme.

  • Robust adaptive nonsingular terminal sliding mode control of MEMS Gyroscope using fuzzy-neural-network compensator
    International Journal of Machine Learning and Cybernetics, 2016
    Co-Authors: Wei Yan, Shixi Hou, Weifeng Yan, Juntao Fei
    Abstract:

    To attenuate the effect of time-varying parameters, quadrature errors, and external disturbances and realize finite-time control, a robust adaptive nonsingular terminal sliding mode (NTSM) tracking control scheme using fuzzy-neural-network (FNN) compensator is presented for micro-electro-mechanical systems (MEMS) vibratory Gyroscopes in this paper. By introducing a nonsingular terminal sliding mode manifold, a novel terminal sliding mode controller is designed for MEMS Gyroscopes, while ensuring the control system could reach the sliding surface and converge to equilibrium point in a finite period of time from any initial state. In the presence of unknown model uncertainties and external disturbances, an adaptive fuzzy-neural-network controller is employed to compensate such system nonlinearities and improve the tracking performance. Online fuzzy-neural-network weight tuning algorithms are derived in the sense of Lyapunov stability theorem to guarantee the network convergence as well as stable control performance. Numerical simulations for a MEMS Gyroscope are provided to justify the claims of the proposed adaptive fuzzy-neural-network control scheme and demonstrate the satisfactory tracking performance and robustness.

  • Adaptive control of MEMS Gyroscope using global fast terminal sliding mode control and fuzzy-neural-network
    Nonlinear Dynamics, 2014
    Co-Authors: Juntao Fei, Wei Yan, Weifeng Yan
    Abstract:

    An adaptive control of MEMS Gyroscope using global fast terminal sliding mode control (GTSMC) and fuzzy-neural-network (FNN) is presented for micro-electro-mechanical systems (MEMS) vibratory Gyroscopes in this paper. This approach gives a new global fast terminal sliding surface, which will guarantee that the designed control system can reach the sliding surface and converge to equilibrium point in a shorter finite time from any initial state. In addition, the proposed adaptive global fast terminal sliding mode controller can real-time estimate the angular velocity and the damping and stiffness coefficients. Moreover, the main feature of this scheme is that an adaptive fuzzy-neural-network is employed to learn the upper bound of model uncertainties and external disturbances, so the prior knowledge of the upper bound of the system uncertainties is not required. All adaptive laws in the control system are derived in the same Lyapunov framework, which can guarantee the globally asymptotical stability of the closed-loop system. Numerical simulations for a MEMS Gyroscope are investigated to demonstrate the validity of the proposed control approaches.

Weifeng Yan - One of the best experts on this subject based on the ideXlab platform.

  • Robust adaptive nonsingular terminal sliding mode control of MEMS Gyroscope using fuzzy-neural-network compensator
    International Journal of Machine Learning and Cybernetics, 2016
    Co-Authors: Wei Yan, Shixi Hou, Weifeng Yan, Juntao Fei
    Abstract:

    To attenuate the effect of time-varying parameters, quadrature errors, and external disturbances and realize finite-time control, a robust adaptive nonsingular terminal sliding mode (NTSM) tracking control scheme using fuzzy-neural-network (FNN) compensator is presented for micro-electro-mechanical systems (MEMS) vibratory Gyroscopes in this paper. By introducing a nonsingular terminal sliding mode manifold, a novel terminal sliding mode controller is designed for MEMS Gyroscopes, while ensuring the control system could reach the sliding surface and converge to equilibrium point in a finite period of time from any initial state. In the presence of unknown model uncertainties and external disturbances, an adaptive fuzzy-neural-network controller is employed to compensate such system nonlinearities and improve the tracking performance. Online fuzzy-neural-network weight tuning algorithms are derived in the sense of Lyapunov stability theorem to guarantee the network convergence as well as stable control performance. Numerical simulations for a MEMS Gyroscope are provided to justify the claims of the proposed adaptive fuzzy-neural-network control scheme and demonstrate the satisfactory tracking performance and robustness.

  • Adaptive control of MEMS Gyroscope using global fast terminal sliding mode control and fuzzy-neural-network
    Nonlinear Dynamics, 2014
    Co-Authors: Juntao Fei, Wei Yan, Weifeng Yan
    Abstract:

    An adaptive control of MEMS Gyroscope using global fast terminal sliding mode control (GTSMC) and fuzzy-neural-network (FNN) is presented for micro-electro-mechanical systems (MEMS) vibratory Gyroscopes in this paper. This approach gives a new global fast terminal sliding surface, which will guarantee that the designed control system can reach the sliding surface and converge to equilibrium point in a shorter finite time from any initial state. In addition, the proposed adaptive global fast terminal sliding mode controller can real-time estimate the angular velocity and the damping and stiffness coefficients. Moreover, the main feature of this scheme is that an adaptive fuzzy-neural-network is employed to learn the upper bound of model uncertainties and external disturbances, so the prior knowledge of the upper bound of the system uncertainties is not required. All adaptive laws in the control system are derived in the same Lyapunov framework, which can guarantee the globally asymptotical stability of the closed-loop system. Numerical simulations for a MEMS Gyroscope are investigated to demonstrate the validity of the proposed control approaches.

Wei Yan - One of the best experts on this subject based on the ideXlab platform.

  • Robust adaptive nonsingular terminal sliding mode control of MEMS Gyroscope using fuzzy-neural-network compensator
    International Journal of Machine Learning and Cybernetics, 2016
    Co-Authors: Wei Yan, Shixi Hou, Weifeng Yan, Juntao Fei
    Abstract:

    To attenuate the effect of time-varying parameters, quadrature errors, and external disturbances and realize finite-time control, a robust adaptive nonsingular terminal sliding mode (NTSM) tracking control scheme using fuzzy-neural-network (FNN) compensator is presented for micro-electro-mechanical systems (MEMS) vibratory Gyroscopes in this paper. By introducing a nonsingular terminal sliding mode manifold, a novel terminal sliding mode controller is designed for MEMS Gyroscopes, while ensuring the control system could reach the sliding surface and converge to equilibrium point in a finite period of time from any initial state. In the presence of unknown model uncertainties and external disturbances, an adaptive fuzzy-neural-network controller is employed to compensate such system nonlinearities and improve the tracking performance. Online fuzzy-neural-network weight tuning algorithms are derived in the sense of Lyapunov stability theorem to guarantee the network convergence as well as stable control performance. Numerical simulations for a MEMS Gyroscope are provided to justify the claims of the proposed adaptive fuzzy-neural-network control scheme and demonstrate the satisfactory tracking performance and robustness.

  • Adaptive control of MEMS Gyroscope using global fast terminal sliding mode control and fuzzy-neural-network
    Nonlinear Dynamics, 2014
    Co-Authors: Juntao Fei, Wei Yan, Weifeng Yan
    Abstract:

    An adaptive control of MEMS Gyroscope using global fast terminal sliding mode control (GTSMC) and fuzzy-neural-network (FNN) is presented for micro-electro-mechanical systems (MEMS) vibratory Gyroscopes in this paper. This approach gives a new global fast terminal sliding surface, which will guarantee that the designed control system can reach the sliding surface and converge to equilibrium point in a shorter finite time from any initial state. In addition, the proposed adaptive global fast terminal sliding mode controller can real-time estimate the angular velocity and the damping and stiffness coefficients. Moreover, the main feature of this scheme is that an adaptive fuzzy-neural-network is employed to learn the upper bound of model uncertainties and external disturbances, so the prior knowledge of the upper bound of the system uncertainties is not required. All adaptive laws in the control system are derived in the same Lyapunov framework, which can guarantee the globally asymptotical stability of the closed-loop system. Numerical simulations for a MEMS Gyroscope are investigated to demonstrate the validity of the proposed control approaches.

Gianluca Piazza - One of the best experts on this subject based on the ideXlab platform.

  • novel on chip rotation detection based on the acousto optic effect in surface acoustic wave Gyroscopes
    Optics Express, 2018
    Co-Authors: Mohamed Mahmoud, Msi Khan, Ashraf Mahmoud, Tamal Mukherjee, James A. Bain, Gianluca Piazza
    Abstract:

    An Acousto-Optic Gyroscope (AOG) consisting of a photonic integrated device embedded into two inherently matched piezoelectric surface acoustic wave (SAW) resonators sharing the same acoustic cavity is presented. This constitutes the first demonstration of a micromachined strain-based optomechanical Gyroscope that uses the effective index of the optical waveguide due to the acousto-optic effect rather than conventional displacement sensing. The theoretical analysis comparing various photonic phase sensing techniques is presented and verified experimentally for the cases based on a Mach-Zehnder interferometer, as well as a racetrack resonator. This first prototype integrates acoustic and photonic components on the same lithium niobate on insulator (LNOI) substrate and constitutes the first proof of concept demonstration of the AOG. This approach enables the development of a new class of micromachined Gyroscopes that combines the advantages of both conventional microscale vibrating Gyroscopes and optical Gyroscopes.

  • Acousto-optic Gyroscope
    2018 IEEE Micro Electro Mechanical Systems (MEMS), 2018
    Co-Authors: Ashraf Mahmoud, Lutong Cai, Msi Khan, Tamal Mukherjee, James A. Bain, Mohamed Mahmoud, Gianluca Piazza
    Abstract:

    An Acousto-Optic Gyroscope (AOG) consisting of a Mach-Zehnder interferometer (MZI) embedded into two inherently matched piezoelectric SAW resonators sharing the same cavity is presented. This constitutes the first demonstration of a MEMS strain-based optomechanical Gyroscope that uses refractive index change due to the acousto-optic effect rather than conventional displacement sensing. This first prototype integrates acoustic and photonic components on the same lithium niobate on insulator (LNOI) substrate and showcases how optical detection can provide greater stability compared to acousto-electric transduction. This approach enables the development of a new class of MEMS-based Gyroscopes that combines the advantages of both conventional MEMS and optical Gyroscopes.

Kouichi Ohwada - One of the best experts on this subject based on the ideXlab platform.

  • Micromachined vibrating rate Gyroscope with independent beams for the drive and detection modes
    Sensors and Actuators A: Physical, 2000
    Co-Authors: Yoichi Mochida, Masaya Tamura, Kouichi Ohwada
    Abstract:

    We investigated the mechanical coupling between the drive and detection modes of micromachined vibrating rate Gyroscopes, and designed and fabricated a Gyroscope with a new structure to reduce the coupling. The coupling of the oscillator was measured using a new measurement system with a two-dimensional laser displacement meter. The oscillations were found to have an elliptical motion due to the coupling. When the frequency mismatch was reduced by means of electrostatic frequency tuning with a DC bias voltage, the coupling increased. The new structure, which has independent beams for the drive and detection modes, exhibited weaker coupling; and the resolution was 0.07°/s at a band width of 10 Hz.