Abstract
This paper presents a novel method that combines fuzzy wavelet network with sliding mode control (FWNSM) to track the maximum power point (MPPT) for a photovoltaic pumping system. For the best use, the photovoltaic (PV) generator must operate at its maximum power point (MPP). Traditional sliding mode control (SMC) can be used to achieve the robust tracking. However, in the presence of large uncertainties the SMC produces the chattering phenomenon due to the higher needed switching gain. In order to reduce this gain, fuzzy wavelet network (FWN) is used for the estimation of model unknown parts and hence enable a lower switching gain to be used. This FWN is trained online using the back-propagation (BP) algorithm. A second robust control term is added to compensate the FWN errors. Particle swarm optimisation (PSO) algorithm is used in this study to optimise the learning rate of BP algorithm in order to improve the network performance in term of the speed of convergence. The stability and effectiveness of the proposed method are proved by Lyapunov method and the simulations are given to demonstrate the performance of the proposed approach by the comparison with traditional SMC.
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