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The design and development of eco-friendly renewable energy sources is a critical process in the power generation system. Power generation of photovoltaic system depend on temperature and irradiation. Variation of atmospheric conditions need to find points for every instant on V-I characteristics of PV in which maximum power transfer from source to load is achieved. This work deals with Maximum Power Point Tracking (MPPT) method based on Adaptive Neuro Fuzzy Interference System (EANFIS) in standalone operation. The novelty is introduced in the design of inverter, motor selection, and maximum power point tracking. Quasi-Z-source inverter (qZSI) is designed with Z-shaped impedance network to continuously draw constant current from solar panel. MPPT enhance the efficiency of PV panel via load matching; however, it may be affected by environmental changes. Hence, an EANFIS-based MPPT technique is used in the proposed work to confirm maximum power delivery to current motor. The proposed method is the combination of ParticleSwarmOptimization (PSO) and Adaptive Neuro Fuzzy Inference System (ANFIS). Training stage of ANFIS is optimized by PSO to handle switching angle of Multi-Level Inverter (MLI) and generate harmonic-less control voltage, hence named Enhanced ANFIS (EANFIS). Voltage and current control of solar panel decide maximum power generation which is verified using Simulink and practical environment. Thus, EANFIS-based MPPT technique achieved the maximum tracking efficiency of 94% which is better than other comparison methods, namely P&O, RBFNN, ANN, and IDISMC. [ABSTRACT FROM AUTHOR]

An effort is made to design the fuzzy proportional-derivative (PD) plus I controller for a nonlinear cruise control system in automobiles, which provides adaptive capability in set-point tracking performance. A cruise control system has been considered as a nonlinear first order plus delay time model to exhibit the control behaviour involved in both conventional proportional-integral-derivative (PID) control and fuzzy control. The paper demonstrates the design of fuzzy PD plus I controller including comparative investigation with control structures like PID, I - PD, and PI - D using Simulink modelling. Real-time implementation has been carried out on a robotic prototype using Arduino UNO micro-controller. Based on the performance measures such as integral absolute error (IAE) and integral squared error (ISE), the proposed fuzzy PD plus I structure shows superior performance on servo and regulatory problems in the cruise control system. This control structure avoids integral windup issues and to suppress the derivative kick in the cruise control system. [ABSTRACT FROM AUTHOR]