Dissertation > Excellent graduate degree dissertation topics show

Control System Research of BLDCM Based on Artificial Neural Network

Author: XiaoGuang
Tutor: GaoXianWen
School: Northeastern University
Course: Control Theory and Control Engineering
Keywords: Brushless DC motor Flux density BP neural network Genetic Algorithms EMF forecast
CLC: TM33
Type: Master's thesis
Year: 2008
Downloads: 216
Quote: 0
Read: Download Dissertation

Abstract


Permanent magnet brushless DC motor (BLDCM) , with the motor control technology, power electronics and microelectronics technology and the emergence of a new motor . In a brushless DC motor , the motor parameters ( EMF , etc. ) that affect motor performance prediction of important factors. In the traditional method of brushless DC motor and control system simulation, the flux density is usually assumed by sinusoidal or trapezoidal distribution, but in fact permanent magnet brushless DC motor flux density distribution of a non- linear , so this kinds of assumptions and the actual large errors. Conventional brushless DC motor (BLDCM) systems typically employ PI control speed . As the motor during operation , the motor parameters and external disturbances is constantly changing , so with ordinary PI controller is difficult to achieve a good result . In response to these problems , this paper analyzes the mathematical model of brushless DC motor based on the first established based on MATLAB / SIMULINK platform brushless DC motor simulation model , and uses the current and speed dual- loop control system to control it. Secondly, this paper, BP neural network has arbitrary precision approximation nonlinear properties, the genetic algorithm and BP neural network combined with the brushless DC motor EMF were predicted predicted results show that the algorithm predicted brushless DC motor EMF is more accurate. Finally, in order to improve the brushless DC motor speed control system performance , this paper will single neuron network and PID combine to form a single neuron adaptive PID controller in order to overcome the deficiencies of conventional PID control , simulation results show that the neural network adaptive PID controller is better than traditional PID controller.

Related Dissertations

  1. Research on Feature Extraction and Classification of Tongue Shape and Tooth-Marked Tongue in TCM Tongue Diagnosis,TP391.41
  2. Municipal tourism land use planning environmental impact assessment,X820.3
  3. Development of the on-line Training and Examination System of Army,TP311.52
  4. Designs and Applications of Fuzzy Synthetic Evaluation Models Based on Parallel Algorithms,TP18
  5. Genetic Algorithm in logistics and warehousing Optimization Research,F259.2
  6. Research on Routing Algorithmin Sensor Networks Based on Cluster with Mobile Sink,TP212.9
  7. Research and Implement of the Theme Crawler for Automotive Industry,TP391.3
  8. Decision Support System of Vehicle Scheduling in Double Level Garage,TP242
  9. Research of the Electrode Lifting Hydraulic System of the 60t Electric Arc Furnace,TF748.41
  10. Development of Electric Actuator Servo System,TM921.541
  11. Multi-Objective Optimization Research on Construction Project Management,TU71
  12. A Research on Power Coal Blending Model of Bituminous Coal Blended with Indonesian Coal,TK227.1
  13. Research of a Medium and Long-term Load Forecasting Method,TM715
  14. Life cycle assessment and its application in the tire industry,F426.72
  15. Efficient brushless DC motor control system design and research,TM33
  16. Data mining and decision support technology applied research in hospitals,TP311.13
  17. ARM9-based image acquisition and tank container number recognition system design,TP274.2
  18. 120 Tons Gantry Crane Structural Analysis and Girder Structural Optimization Based on Genetic Algorithm,TH213.5
  19. Application of QSAR and Molcular Docking Studies in Medicinal Analytical Chemistry,R917
  20. Multi-objective Genetic Algorithm Based Cognitive radio decision engine,TN925
  21. DsPIC based brushless DC motor control system design,TM33

CLC: > Industrial Technology > Electrotechnical > Motor > DC motor
© 2012 www.DissertationTopic.Net  Mobile