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Research On Parameter Identification Method Of Motor Drive System

Posted on:2009-01-12Degree:DoctorType:Dissertation
Country:ChinaCandidate:J WuFull Text:PDF
GTID:1102360272977766Subject:Motor and electrical appliances
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In the early stages, the majority of variable-speed drives for the transmission field uses the dc drives.However, from the late 1960s, ac drives are much superior to dc drives in the industrial application field, ac drives become increasingly cheap and popular. As an important part of electric drive, the permanent magnet AC servo system plays a more and more significant role in industry, agriculture, aerospace and other fields. The main disadvantage of PMSM is its pulsating torque. Torque smoothness is an essential requirement in a wide range of high-performance motion applications. So the compensation of pulsating torque is very importance for the application of the permanent magnet AC servo system.Affecting the motor performance as parameters change in the motor drive system, for example PMSM, in order to improve the performance of the PMSM control system by parameter identification, the magnetic field oriented control methods, parameter identification, the application of neural network and extended kalman filter in vector control, the compensation of pulsating torque, and the means to increase the performance of motor have been studied deeply in this paper based on the platform of all-digital PMAC servo control system.This paper analyses the problem of the compensating techniques of pulsating torque by parameter identification in basic configuration of the permanent magnet AC servo system. The techniques of pulsating torque minimization based on the improvement of control system are focused on. Several parameter identification algorithms are proposed in this paper. The main contribution of this dissertation is summarized as following:1. A standard model of PMSM deduced from the stator flux linkage equations is studied. The models of PMSM in theα-β(stator two phases) frame and d-q (synchronous rotating) frame are proposed by transformation.2. This paper analyses the work principle of PMSM i_d=0 vector control. The basic conception of i_d=0 magnetic field oriented control is introduced. The analysis model of the total control system is described.3. An adaptive control algorithm of pulsating torque is described using the theory of model reference adaptive system. Considering the time variability of the parameters of PMSM, the methods make real-time identification of the parameters to retain the permanent magnet AC servo system. The performance of the system is simulated using Matlab/Simulink toolbox; the simulation results show the MRAS-based field oriented control system has good static and dynamic performance.4. Artificial neural networks have mighty learning ability. After being properly trained, the multiplayer networks are capable of approximating any nonlinear functions with any precision. Therefore it has become a powerful tool in nonlinear system identification field. A novel neural network control strategy with motor speed target function is proposed for PMSM. This dissertation also establishes algorithm of load torque disturbance identification and the compensations of control system. The performance of the system is simulated using Matlab software. The simulation results show that a novel neural network control strategy with motor speed teacher controller and target function has strong robustness, good dynamic performances and good tracking performance.5. Some parameters will vary with the temperature rise and magnetic saturation, and they must be real time identified. Just using stator currents, stator voltage, and velocity, the method proposed in the paper makes real time identification PMSM parameters based on the least squares identification algorithm. The method doesn't use the flux signal, avoiding the coupling between the flux observation and the parameter identification. After identifying the parameters, because of the multiplication terms of state variables, the PMSM model is still the non-liner state equations. To estimate the state variables of PMSM model and gain the parameter identification of motor, the paper proposes a method to estimate them using extended kalman filter. The simulation of the method gets satisfied results.6. The paper proposes all-digital PM AC servo control system based on the DSP design system for digital motor control. Software programs carry out extended kalman filter algorithm to estimate the resistance and flux, and space vector pulse width modulation algorithm based on magnetic field oriented. The satisfied experimental results prove the extended kalman filter algorithm can real time estimate the resistance and flux very accurately, and based on which the permanent magnet AC servo system has good static dynamic performance.
Keywords/Search Tags:Permanent Magnet Synchronous Motor (PMSM), Vector Control, Parameter Identification, Model Reference, Adaptive System, Artificial Neural Networks, Extended Kalman Filter, Space Vector Torque Control, Pulsating Torque
PDF Full Text Request
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