Adaptive neural compensation scheme for robust tracking of MEMS gyroscope

Fei, Juntao; Yang, Yuzheng

doi:10.1109/icsmc.2012.6377956

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…To compare the performance of the proposed controller and the neural compensator without sliding mode method in [16], Figures 12,13, and 14 show the response with the neural compensator under the same gyroscope parameters and disturbances introduced herein.…”

Section: Simulation Analysismentioning

confidence: 99%

“…Huang et al [15] developed a novel RBF sliding mode controller for a dynamic absorber. An adaptive neural compensation scheme without sliding mode method for tracking control of MEMS gyroscope was proposed [16]. In [17], robust adaptive sliding mode control is utilized to estimate the angular velocity of MEMS triaxial gyroscope, and neural network is adopted to estimate the upper bound of system nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

“…In [18], an input-output linearization technique is incorporated into the neural adaptive tracking control to cancel the nonlinearities and the adaptive neural network is used to perform the linearization control law. In this paper, different from [16][17][18], neural controller is proposed to approximate the unknown system model and sliding mode controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. In order to improve the tracking performance of MEMS gyroscope, a robust adaptive control strategy of MEMS gyroscope using neural sliding compensator is developed.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation

Yang

Fei

2014

Journal of Applied Mathematics

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An adaptive sliding controller using radial basis function (RBF) network to approximate the unknown system dynamics microelectromechanical systems (MEMS) gyroscope sensor is proposed. Neural controller is proposed to approximate the unknown system model and sliding controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. Online neural network (NN) weight tuning algorithms, including correction terms, are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. The tracking error bound can be made arbitrarily small by increasing a certain feedback gain. Numerical simulation for a MEMS angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory tracking performance and robustness.

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Section: Simulation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation

Yang

Fei

2014

Journal of Applied Mathematics

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“…Compared with traditional results of MEMS control [15][16][17], in this paper, minimal-learning-parameter technique is employed for controller design to reduce computation burden.…”

Section: Remarkmentioning

confidence: 99%

“…Thus, an adaptive control strategy using radial basis function (RBF) network/Fuzzy Logic System compensator is presented for robust tracking of MEMS gyroscope in the presence of model uncertainties and external disturbances to compensate such system nonlinearities and improve the tracking performance in [17][18][19]. However, in practical application, large amount of update parameters results in the computation burden of online learning.…”

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 MEMS gyroscope using fully tuned RBF neural network

Fei

2015

Neural Comput & Applic

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Adaptive neural compensation scheme for robust tracking of MEMS gyroscope

Cited by 7 publications

References 14 publications

Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation

Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation

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

Adaptive control of MEMS gyroscope using fully tuned RBF neural network

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