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• W3036847688 abstract "This article proposes the idea of fault classification-based control for the steady-state operation of unmanned surface vehicles (USVs). The idea of fault classification is achieved with the help of wavelet transforms and support vector machines, and the control is performed using a wavelet fuzzy controller. Initially, a brief idea of faults that affect the stable operation of USVs is identified. Furthermore, the surge and sway translational motion of USVs are realized with the help of a ball balancer setup. The fault data are measured in terms of plate angle, ball position, and motor operating voltage for developing the fault classifier. The proposed algorithm depicted improved classification accuracy when compared with conventional methods. To accommodate the operation of the system as per the operating state, a wavelet-based fuzzy controller is proposed. The proposed controller solves the problem of position tracking and balancing for ball and plate system with high precision, hence achieving the stable operation." @default.
• W3036847688 created "2020-06-25" @default.
• W3036847688 creator A5031425996 @default.
• W3036847688 creator A5055996071 @default.
• W3036847688 date "2021-08-01" @default.
• W3036847688 modified "2023-10-04" @default.
• W3036847688 title "Condition Monitoring Based Control Using Wavelets and Machine Learning for Unmanned Surface Vehicles" @default.
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• W3036847688 doi "https://doi.org/10.1109/tie.2020.3001855" @default.
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