Search

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]

The scope of this research is to design and fuse the sensors used in the self-balancing vehicle through Adaptive Neuro-Fuzzy Inference systems (ANFIS) algorithm to optimize the output. The self-balancing vehicle is a wheeled inverted pendulum, which is extremely complex, nonlinear and unstable. Homogeneous and Heterogeneous sensors are involved in this sensor fusion research to identify the best feasible value among them. The data fusion algorithm present inside the controller of the self-balancing vehicle makes the inputs of the homogeneous sensors and heterogeneous sensors separately for ameliorate surrounding perception. Simulation is performed by modeling the sensors in Simulink. The outcomes specifies that the data fusion algorithm allocates minimal root mean square error (RMSE) and mean absolute percentage error (MAPE) when analyzed and compared with that of every sensor in the system. Finally, the output signals of these sensors are examined and viewed along with noise signal and the actual signal is isolated from the noise signal by applying extended Kalman filter. This propounded technique of ANFIS based fusion algorithm has improved RMSE for both homogeneous sensors and heterogeneous type sensors. Robotic systems may execute several control strategies in various proximity levels based on the performance of the data fusing algorithm. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm is well known and widely used optimization method based on swarm intelligence, and it is inspired by the behavior of honeybees searching for a high amount of nectar from the flower. However, this algorithm has not been exploited sufficiently. This research paper proposes a novel method to analyze the exploration and exploitation of ABC. In ABC, the scout bee searches for a source of random food for exploitation. Along with random search, the scout bee is guided by a modified genetic algorithm approach to locate a food source with a high nectar value. The proposed algorithm is applied for the design of a nonlinear controller for a continuously stirred tank reactor (CSTR). The statistical analysis of the results confirms that the proposed modified hybrid artificial bee colony (HMABC) achieves consistently better performance than the traditional ABC algorithm. The results are compared with conventional ABC and nonlinear PID (NLPID) to show the superiority of the proposed algorithm. The performance of the HMABC algorithm-based controller is competitive with other state-of-the-art meta-heuristic algorithm-based controllers in the literature. [ABSTRACT FROM AUTHOR]

PIC microcontroller has a vital role in various types of controllers and nowadays the presence of PIC microcontroller is unavoidable in most control applications. This paper describes the enhancement of soft starting of induction motors using PIC microcontroller. Soft starting is required for the induction motors with reduced applied voltages so that the peak starting current is reduced and the startup of the motor is smooth with controlled torque and reduced mechanical vibrations, reduced starting current with correspondingly reduced bus voltage drops. While many of the available schemes of soft starting offer better results in view of smooth starting process, the proposed methodology offers soft starting as well as it cares about the source current quality. In contrast to the conventional multistep starting scheme, in this work a continuously controlled starting scheme is proposed. The three phase AC voltage controller topology is used as the core controller. In each of the half cycles, instead of a single conduction period with a single delay angle, the proposed AC voltage controller is switched symmetrically in each half cycle with multiple pulses in each quarter cycle. Symmetrically placed fixed numbers of switching pulses are used. The paper also describes the various time ratio controls namely the phase angle controller, the extinction angle controller and the symmetrical angle controller. The design aspects of the proposed soft starting scheme and the validation of the proposed system in the MATLAB SIMULINK environment are presented in this paper. [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]