Adaptive control of MEMS gyroscope using fully tuned RBF neural network

Fei, Juntao; Wu, Dan

doi:10.1007/s00521-015-2098-2

Cited by 14 publications

(8 citation statements)

References 12 publications

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“…After each component is predicted, the predicted results need to be reconstructed. RBF neural network is a feed forward network with a three-tier structure, namely input layer, hidden layer and output layer (Fei and Wu, 2017;Deng et al, 2018). The input of RBF neural network consists of predicted values of each component.…”

Section: The Reconstruction Of the Rbf Neural Networkmentioning

confidence: 99%

A hybrid model of empirical wavelet transform and extreme learning machine for dissolved oxygen forecasting

Huan¹,

Cao

et al. 2020

IJES

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The accurate predicting trend of dissolved oxygen (DO) can reduce the risks to aquaculture, so a combined nonlinear prediction model based on empirical wavelet transform (EWT) and extreme learning machine (ELM) optimised by adaptive disturbance particle swarm optimisation (ADPSO) is proposed. First of all, DO series are decomposed into a term of relatively subsequence by EWT, secondly, the decomposed components are reconstructed using the C-C method, and thirdly an ELM prediction model of every component is established. At last, the predicted values of DO datasets are calculated by using RBF to reconstruct the forecasting values of all components. This model is tested in the special aquaculture farm in Liyang City, Jiangsu Province. Results indicate that the proposed prediction model of EWT-ELM has better performance than WD-ELM, EMD-ELM, ELM and EWT-BP. The research shows that the combined forecasting model can effectively extract the sequence characteristics, and can provide a basis for decision-making management of water quality, which has certain application value.

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Section: The Reconstruction Of the Rbf Neural Networkmentioning

confidence: 99%

A hybrid model of empirical wavelet transform and extreme learning machine for dissolved oxygen forecasting

Huan¹,

Cao

et al. 2020

IJES

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“…Design of gyroscopes can be improved by efficient control approaches which are completely capable to eliminate error signals (Hiller et al, 2017). In the recent years, many control techniques for instance, fuzzy control (Yan et al, 2017), adaptive approach (Fei and Wu, 2017; Shao and Shi, 2020), sliding mode control (SMC) (Fei and Batur, 2009), hybrid robust control (Rahmani et al, 2020), observer-based back-stepping control (Lu and Fei, 2017), and model-based predictive control (Wu et al, 2019) have been proposed for the stabilization of the states of MEMS. But these approaches can only stabilize the system asymptotically.…”

Section: Introductionmentioning

confidence: 99%

Nonsingular terminal sliding mode control for micro-electro-mechanical gyroscope based on disturbance observer: Linear matrix inequality approach

Jafari

Mobayen

Roth

et al. 2021

Journal of Vibration and Control

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The aim of this article is to design a nonsingular terminal sliding mode control method based on disturbance observer for the stabilization of the micro-electro-mechanical systems under lumped perturbation. By using the nonsingular terminal sliding mode control scheme, the state trajectories of the system achieve the switching surface and approach to the origin in the finite time. Also, by utilizing the disturbance observer, the finite-time convergence of disturbance error is assured. In the process of design, the optimized coefficients of the sliding surface are calculated in the form of linear matrix inequality. Simulation results for a micro-electro-mechanical gyroscope are illustrated to exhibit the validity of the planned approach in comparison with the other methods.

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“…Aiming at the fact that the industrial robot control system is inevitably affected by random noise in practical work, and a design of RBF neural network and PD compound controller based on Kalman filter is proposed [21]. In order to eliminate the adverse effect of external interference and model uncertainty on the control system, a new intelligent control algorithm based on radial basis function neural network is designed for the control of MEMS gyroscopes [22]. According to the characteristics of nonlinear U model, this paper proposes the parallel control system based on RBF neural network and PID, and analyses the importance of Newton iteration algorithm in the control system.…”

Section: Introductionmentioning

confidence: 99%

Research on parallel nonlinear control system of PD and RBF neural network based on U model

2020

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The modelling problem of nonlinear control system is studied, and a higher generality nonlinear U model is established. Based on the nonlinear U model, RBF neural network and PD parallel control algorithm are proposed. The difference between the control input value and the output value of the neural network is taken as the learning target by using the online learning ability of the neural network. The gradient descent method is used to adjust the PD output value, and ultimately track the ideal output. The Newton iterative algorithm is used to complete the transformation of the nonlinear model, and the nonlinear characteristic of the plant is reduced without loss of modelling precision, consequently, the control performance of the system is improved. The simulation results show that RBF neural network and PD parallel control system can control the nonlinear system. Moreover, the control system with Newton iteration can improve the control effect and anti-interference performance of the system.

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Adaptive control of MEMS gyroscope using fully tuned RBF neural network

Cited by 14 publications

References 12 publications

A hybrid model of empirical wavelet transform and extreme learning machine for dissolved oxygen forecasting

A hybrid model of empirical wavelet transform and extreme learning machine for dissolved oxygen forecasting

Nonsingular terminal sliding mode control for micro-electro-mechanical gyroscope based on disturbance observer: Linear matrix inequality approach

Research on parallel nonlinear control system of PD and RBF neural network based on U model

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