Dissertation > Excellent graduate degree dissertation topics show

The Study of an ANN Inverse Decoupling Control of PMSM Motors Based on DSP

Author: ZhouTong
Tutor: LiuGuoHai
School: Jiangsu University
Course: Power Electronics and Power Drives
Keywords: Permanent Magnet Synchronous Motor inverse system neural network linearization decoupling
CLC: TM341
Type: Master's thesis
Year: 2009
Downloads: 244
Quote: 1
Read: Download Dissertation

Abstract


Permanent Magnet Synchronous Motors(PMSM) are core parts of many high precision,high efficiency and low power automatic control systems. Improving on the strategies of PMSM control can enhance the performances of those automatic systems directly.This dissertation focuses on some problems in decoupling control strategies,such as that only can realize static decoupling,run short of both the robustness on parameter variation and the ability to resist load disturbance.These strategies depend on Permanent Magnet Synchronous Motor (PMSM) models.Combine neural networks with inverse system method,the neural network inverse system method of Permanent Magnet Synchronous Motor control is proposed.The Permanent Magnet Synchronous Motor,which is a multi-variable,strongly coupling and nonlinear object,is linearised and decoupled into two SISO subsystems.The influence caused by parameter variation and load disturbance decrease evidently.This method provides a new approach for high performance control of Permanent Magnet Synchronous Motor. Main progresses in this dissertation are as follows:First,the decoupling control theory based on inverse system method is studied by using the linear algebraic method of nonlinear systems.The necessary and sufficient conditions for invertibility of nonlinear system are developed,and the construction algorithms for pseudo-linear subsystems are given respectively.Secondly,the invertibility and decoupling property of Permanent Magnet Synchronous Motor are analyzed systematically and thoroughly.The structures of the state feedback combined with the input integral inverse system,which achieve linearization and decoupling of Permanent Magnet Synchronous Motor, are put forward.The influence of motor parameter variation and load disturbance to the decoupling control performance is discussed,which shows that the analytical inverse method is not able to achieve the high performance for Permanent Magnet Synchronous Motor control.In order to eliminate the influence resulted from parameter variation and load disturbance,a neural network inverse system method is proposed to realize the decoupling control of Permanent Magnet Synchronous Motor.The structure of neural network inverse system,the identification approach and realization steps to obtain the neural network inversion are given.After the neural network inversion is connected before the Permanent Magnet Synchronous Motor in series,the Permanent Magnet Synchronous Motor is decoupled into two SISO pseudo-linear integral subsystems.Then,close-loop linear controllers are designed.The control system has good robustness to parameter variation and strong adaptability to load disturbance.Finally,the proposed method is validated through a controlling experimental platform which is based on digital signal processor(DSP) and intelligent power module(IPM).The control performance is satisfying,which shows that the neural network inverse system method is an effective and applicative method for the control of Permanent Magnet Synchronous Motor.

Related Dissertations

  1. Research on the 6-Dof Fault Tolerant Control of the Vibration Isolation Platform with Eight Actuators,TB535.1
  2. Development of the Platform for Compressor Optimization Design and Aerodynamic Optimization Design in the Transonic Compressor,TH45
  3. Research on Control of Precision Centrifuge Dynamic Balance System,TH113.25
  4. Parameterization Design and Research of Permanent Magnet Synchronous with Fractional-slot and Concentrated Winding,TM341
  5. Research on High Efficiency Interior Permanent Magnet Synchronous Motor,TM341
  6. Research and Improvement of Multi-Level Modulation Self-Adaptive Digital Predistortion Algorithm,TN722.75
  7. Research on Feature Extraction and Classification of Tongue Shape and Tooth-Marked Tongue in TCM Tongue Diagnosis,TP391.41
  8. Study on Virtual Detector of Infrared Hyper-Spectral Image,TP391.41
  9. The Application of Fuzzy Control and Neural Network in Planar Double Inverted Pendulum,TP273.4
  10. Research of Visual Servoing in 4-DOF Robotic Manipulator,TP242.6
  11. Research on Visual Servo System of Mechanical ARM,TP242.6
  12. Design of Weapon Detection Device Control System,TP183
  13. Municipal tourism land use planning environmental impact assessment,X820.3
  14. Research on the Inteligent System of High Performance Concrete Mix Design in Zhujiang Triangle Area,TU528
  15. Study on Taste Characteristic of Taste Peptide Enzymatic Production from Oyster Base on A Neural Network Method,TS254.4
  16. Smart Control Research in Paddy Drying Based on BP Neural Network,S226.6
  17. The Research on Quadratic System Decoupling,O175
  18. The Research on Nonsingular Solution of the Sylvester Equation Based on Quadratic System Decoupling,O175
  19. BP network optimization based on genetic algorithm optimization of the biodiesel process,TE667
  20. Fire Fighting System Research for the Offshore Platform,U698.4
  21. The Research on Evaluation of Living Status Systems of Expressway Relocated People,D523

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