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- W3000092979 abstract "An adaptive back stepping Radial Basis Function Neural Network Controller (RBFNNC) is proposed for the Maximum Power Point Tracking (MPPT) in Photo Voltaic (PV) systems to track the desired output voltage. The computational complexity of the neural network is eluded by the use of radial basis function. The online weight update of the radial basis function neural network is designed for the closed loop system based on the Lyapunov stability analysis to obtain the asymptotically stable system. A comprehensive analysis of the steady state and transient performance is performed and results are compared with that of back stepping controller and adaptive back stepping controller. The performance evaluation confirms that the RBFNNC has robustness and shows its enhanced transient as well as steady state responses over other controllers for wide range of variations in temperature and irradiance of PV systems with uncertainties caused by disturbances." @default.
- W3000092979 created "2020-01-23" @default.
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- W3000092979 date "2019-04-01" @default.
- W3000092979 modified "2023-10-14" @default.
- W3000092979 title "Adaptive Neuralback Stepping Controller for MPPT in Photo Voltaic Systems" @default.
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- W3000092979 doi "https://doi.org/10.1109/incos45849.2019.8951363" @default.
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