Comparative analysis of fuzzy and ANFIS based MPPT controller for wind power generation system

<span lang="EN-US">In recent years, huge developments in wind energy production and meet consumer demand. Numerous researchers have focused on maximum energy generation techniques for the wind system. The main reason for this work is to compare the different smart controllers for the maximum power generation techniques in the wind system. In this article, we developed and modeled a 250-watt wind power system in a MATLAB environment and simulated it in different weather conditions. Based on the simulation results, two intelligent controllers, such as fuzzy and ANFIS, were proposed and compared to obtain the maximum energy generation techniques in the wind system. Finally, the optimal smart controller was chosen based on performance.</span>

Intelligent Cabinet Lock Control System Based on Cloud Technology

With the rapid development of wireless communication and Internet of things, intelligent products have been widely used in living and industrial places. The purpose of this paper is to develop an intelligent controller and intelligent lock cabinet for the monitoring of optical cable handover box. It can monitor the temperature, humidity, fire, flood, vibration and the identity of the personnel in and out of the optical cable box, and send the data information to the management server through wireless communication, so as to realize the purpose of monitoring and management of the optical cable box. The products under development include intelligent controllers, smart keys and electronic locks. The smart key is used in conjunction with the electronic lock.

Study and Investigation of Energy Management Techniques Used in Electric/Hybrid Electric Vehicles

Many pollution-related issues are raising due to the usage of conventional internal combustion engines (ICEs) vehicles. Electric Vehicles/ Hybrid electric vehicles (EVs/HEVs) are the finest solutions to overcome those problems associated with ICE-based vehicles. The EVs are introduced with a signal energy source (SES), which is not a successful attempt, especially during transient vehicles, driving, etc. Multiple energy sources (MES) EVs are introduced to attain better performance than the SES vehicles, which is obtained by combining two sources like battery/fuel cells, ultracapacitor. In this contest, energy management (EMNG) plays a vital role in sharing the load to the sources as per the EVs requirement. In the case of MES-based EVs, the controller always plays a significant role in the related EMNG system because it is the key factor in improving vehicle efficiency. In this article, a study has mainly been done related to several conventional, intelligent controllers and control algorithms to do the proper EMNG between sources present in the EV.

Systematic design of multi-objective enhanced genetic algorithm optimized fractional order PID controller for sensorless brushless DC motor drive

Purpose The puspose of this paper, a novel systematic design of fractional order proportional integral derivative (FOPID) controller-based speed control of sensorless brushless DC (BLDC) motor using multi-objective enhanced genetic algorithm (EGA). This scheme provides an excellent dynamic and static response, low computational burden, the robust speed control. Design/methodology/approach The EGA is a meta-heuristic-inspired algorithm for solving non-linearity problems such as sudden load disturbances, modeling errors, power fluctuations, poor stability, the maximum time of transient processes, static and dynamic errors. The conventional genetic algorithm (CGA) and modified genetic algorithm (MGA) are not very effective in solving the above-mentioned problems. Hence, a multi-objective EGA optimized FOPID (EGA-FOPID) controller is proposed for speed control of sensorless BLDC motor under various conditions such as constant load conditions, varying load conditions, varying set speed (Ns) conditions, integrated conditions and controller parameters uncertainty. Findings This systematic design of the multi-objective EGA-FOPID controller is implemented in MATLAB 2020a with Simulink models for optimal speed control of the BLDC motor. The overall performance of the EGA-FOPID controller is observed and evaluated for computational burden, time integral performance indexes, transient and steady-state characteristics. The hardware experiment results confirm that the proposed EGA-FOPID controller can precisely change the BLDC motor speed is desired range with minimal effort. Research limitations/implications The conventional real time issues such as nonlinearity characteristics, poor controllability and stability. Practical implications It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented. Originality/value It shows the effectiveness of the proposed controllers is completely verified by comparing the above three intelligent optimization algorithms. It is clearly evident that out of these three intelligent controllers, the EGA optimized FOPID controller gives enhanced performance by minimizing the time domain parameters, performance Indices error and convergence time. Also, the hardware experimental setup and the results of the proposed EGA-FOPID controller are presented.

Sensor-less Brushed DC Motor Speed Control with Intelligent Controllers

A Direct Current (DC) Motor is usually supposed to be operated at a desired speed even if the load on the shaft is exposed to changes. One of its applications is in automatic door controllers like elevator automatic door drivers. Initially, to achieve this aim, a closed loop control can be applied. The speed feedback is usually prepared by a sensor (encoder or tachometer) coupled to the motor shaft. Most of these sensors do not always perform well, especially in elevator systems, where high levels of noise, physical tensions of the mobile car, and maintenance technicians walking on the car, make this environment too noisy. This Paper presents a new approach for precise closed loop control of the DC motor speed without a feedback sensor, while the output load is variable. The speed here is estimated by the Back EMF (BEMF) voltage obtained from the armature current. First, it is shown that a PID controller cannot control this process alone, and then intelligent controllers, Fuzzy Logic Controller (FLC) and Adaptive Neuro Fuzzy Inference Systems (ANFIS), assisting PID are applied to control this process. Finally, these controllers’ performance subjected to a variable mechanical load on the motor shaft are compared.

Implementation of a Fuzzy Logic Controller for the Irrigation of Rose Cultivation in Mexico

The design and implementation of a fuzzy logic controller (FLC) are presented, offering a solution to improve the irrigation of rose crops. The objective is to reduce the water consumption and operative costs, taking advantage of intelligent controllers and environmental characteristics in a specific region. Considering that the main controllable variables that affect the growth of plants are relative humidity (RH) and temperature (T), in this study, these variables are used to create a system whose aim is to provide an adequate amount of water for a rose crop in the State of Mexico. The Mamdani method was used for the FLC design and the membership functions, while the area centroid was considered as the defuzzification strategy. After implementing the FLC proposal using a field-programmable gate array (FPGA) in a domestic greenhouse, integrated by an array of [5 × 3] rose plants under natural restrictions, a reduction of 0.2 L per week with respect to the traditional manual irrigation system was found. The proposed design highlights the technological advantages of using a fuzzy logic-controlled irrigation system over traditional methods.