Adaptive RBF Neural Vibration Control of Flexible Structure

Hu, Tongyue; Fei, Juntao

doi:10.1155/2014/956026

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…39 The neural adaptive SMCs are designed to improve the robustness of the system and reduce the system chattering. [40][41][42] In order to develop an efficient steering system for the nonlinear course control problem of USV with parameter uncertainties and external disturbances, a neural adaptive SMC based on radial basis function (RBF) NN is designed in this article. RBF NN has good generalization ability.…”

Section: Introductionmentioning

confidence: 99%

“…The NN which can effectively solve the problems with regard to unknown nonlinear systems attracts many concerns from many researchers. [17][18][19][20][21][22][23][24][39][40][41][42] Using the universal approximation property of the NNs, the influence of the external disturbance and uncertainty can be reduced, and the controller becomes independent of the precise system model information. 39 The neural adaptive SMCs are designed to improve the robustness of the system and reduce the system chattering.…”

Section: Introductionmentioning

confidence: 99%

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Neural adaptive sliding mode controller for unmanned surface vehicle steering system

Wan

Zhang

et al. 2018

Advances in Mechanical Engineering

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Unmanned surface vehicle has the properties such as complexity, nonlinearity, time variability, and uncertainty, which lead to the difficulty of obtaining a precise kinematics model. A neural adaptive sliding mode controller for the unmanned surface vehicle steering system is developed based on the sliding mode control technique and the radial basis function neural network. In the new approach, two parallel radial basis function neural networks are used to reduce the influence of the system uncertainties and eliminate the dependency of the controller on the precise kinematics model of the system. Among these two radial basis function neural networks, one is used to approximate the unknown nonlinear yaw dynamics and the other is used to adjust the control gain as well as realize the variable gain sliding mode control. The weights of the two neural networks are trained online using the sliding surface variable and the control, where the Lyapunov method is used to derive the adaptive laws to ensure the stability of the whole closed-loop system. The proposed adaptive controller is suitable for the steering control at different cruising speeds with bounded external disturbances. The simulation results show that the proposed controller has a good control performance regarding the smooth control, fast response, and high accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural adaptive sliding mode controller for unmanned surface vehicle steering system

Wan

Zhang

et al. 2018

Advances in Mechanical Engineering

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Rice plant disease diagnosing using machine learning techniques: a comprehensive review

2022

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The impact of rice plant diseases has led to a 37% annual drop in rice production. It may happen basically due to the lack of knowledge in identifying and controlling rice plant diseases, but still there isn’t any proper application has been developed which is capable enough to identify these rice plant diseases accurately and control those diseases. In order to identify rice plant disease by an application itself, Convolutional Neural Networks (CNN) can be used. Many of researchers have used CNNs for plant disease identification because of their accuracy in image identification and classification. But, there’s still a handful researches have been conducted regarding the identification of rice plant diseases. This study provides a comprehensive understanding of current rice plant illnesses as well as the Deep Learning approaches used to detect such diseases. It also analyzes several kinds of techniques that have been employed in the literature by analyzing all of them with their benefits and drawbacks. It has discovered the most accurate ways in all levels of the image identification procedure as a consequence of this research, that will be valuable in recognizing rice plant illnesses.

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Adaptive RBF Neural Vibration Control of Flexible Structure

Cited by 2 publications

References 19 publications

Neural adaptive sliding mode controller for unmanned surface vehicle steering system

Neural adaptive sliding mode controller for unmanned surface vehicle steering system

Rice plant disease diagnosing using machine learning techniques: a comprehensive review

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