Brushless DC (BLDC) motors are one of the most interesting motors, not only because of their efficiency, and torque characteristics, but also because they have the advantages of being a direct current (DC) supplied, but eliminating the disadvantages of using Brushes. BLDC motors have a very wide range of speed, so speed control is a very important issue for it. There are a lot of parameters which need to be in focus while talking about a speed controller performance like starting current, starting torque, rise time, etc. There are two main methods for controlling the speed, PID Controllers, and Fuzzy PI controllers. Both are different in complexity and performance. In this paper, the PI and Fuzzy PI speed controllers for the BLDC motors will be proposed. A simulation study is conducted to evaluate the efficiency of the proposed speed controllers. Further, a comparative study is performed to validate the system effectiveness.
Brushless dc motors (BLDC motors) are commonly used nowadays in industry and at many applications according to its very high speed with a very compact size in comparison to the older motors with brushes, moreover the importance of being powered by direct current (DC) and without all disadvantages of using brushes, which is convenient to many applications like hard drivers, CD/DVD players, electric bicycles, electric and hybrid vehicles, CNC machines and Aero modeling. The purpose of this paper is to control the speed of a brushless dc motor by using PID controller, Fuzzy logic controller, and Neuro fuzzy controller. According to these varieties of control techniques which used to control the speed, we have many parameters which used to assess that which controller will be better to use.
Micro Elector Mechanical Systems (MEMS) sensors enable a wide range of different applications in many fields: among others, in the last decade the use of MEMS accelerometers for seismology-related applications has grown exponentially. In this paper, we do a comprehensive review of MEMS accelerometers: Operating Principle, technical specifications, types, performance, Comparison with Geophone. We introduce the applications within seismology and earth sciences related disciplines. We introduce how to use MEMS accelerometer and geophone to build a low Coast earthquake monitoring unit. Moreover, we will discuss the performance of MEMS accelerometer, geophone by conducting a practical experiment to measure one of the explosions that occur near the city of Helwan,
Sensorless Brushless DC (BLDC) motors have many advantages because of their greet characteristics which make them an interested field of research home and abroad. One of the biggest issues which face BLDC motors is the rotor position detection, as they are electrically commutated the rotor position must be detected with or without sensors. The position detection is used for commutation, speed measurement and speed control. The traditional method for BLDC motor rotor position detection which depends on Hall sensors has lots of problems that decrease the system reliability, so sensorless BLDC motor drives are extensively used. The sensorless position detection methods mostly use Zero Crossing Point (ZCP) detection techniques which depend on motor back EMF detection.In this paper, a suggested method of ZCP detection for speed measurement is presented for BLDC motor, to decrease driver cost and increase system reliability, also the ZCP detection method can be for commutation and speed measurement. Simulation results are shown to prove system effectiveness.
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