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Linguistic modeling of complex irregular systems constitutes the heart of many control and decision making systems, and fuzzy logic represents one of the most effective algorithms to build such linguistic models. In this paper, a linguistic (qualitative) modeling approach is proposed. The approach combines the merits of the fuzzy logic theory, neural networks, and genetic algorithms (GA's). The proposed model is presented in a fuzzy-neural network (FNN) form which can handle both quantitative (numerical) and qualitative (linguistic) knowledge. The learning algorithm of an FNN is composed of three phases. The first phase is used to find the initial membership functions of the fuzzy model. In the second phase, a new algorithm is developed and used to extract the linguistic-fuzzy rules. In the third phase, a multiresolutional dynamic genetic algorithm (MRD-GA) is proposed and used for optimized tuning of membership functions of the proposed model. Two well-known benchmarks are used to evaluate the performance of the proposed modeling approach, and compare it with other modeling approaches.

This paper proposes a novel phase-locked loop (PLL) control strategy to synthesize unit vector using the modified synchronous reference frame (MSRF) instead of the traditional synchronous reference frame. The unit vector is used for vector rotation or inverse rotation in vector-controlled three-phase gridconnected converting equipment. The developed MSRF-PLL is fast in transient response compared to standard PLL technique. The performance is robust against disturbances on the grid, voltage wave with harmonic distortion, and noise. The proposed algorithm has been analyzed in detail and was fully implemented digitally using digital signal processor TMS320F2812. The experimental evaluation of the MSRF-PLL in a shunt active power filter confirms its fast dynamic response, noise immunity, and applicability.Index Terms-Active filter, digital signal processor (DSP), phase-locked loop (PLL), synchronous reference frame (SRF).

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