基于MRAS的异步电机无速度传感器矢量控制系统设计与优化

发布时间：2018-05-13 02:14

本文选题：异步电机 + 矢量控制　；参考：《南京邮电大学》2015年硕士论文

【摘要】：在实际的异步电机调速系统中,安装速度传感器存在易受环境影响、成本增加及难以维护等缺陷,而实现无速度传感器的转速辨识能够有效地克服上述缺陷。另外,优化研究异步电机调速系统中PI(比例-积分)调节器参数,对提高系统性能具有重要的意义。为此,本文研究了异步电机矢量控制系统无速度传感器转速辨识以及调节器参数的优化问题,首先采用一种基于改进反电动势的转速模型参考PI自适应辨识方法,实现了异步电机转速的辨识;然后采用改进的量子免疫遗传算法同时优化系统中转速调节器、励磁电流调节器、转矩电流调节器以及转速辨识模块中调节器的参数,以实现系统的优化控制;最后,在Matlab仿真及硬件平台,验证了本文工作的有效性。本文主要工作如下:1、研究了异步电机无速度传感器转速辨识问题。首先,采用一种基于改进反电动势的转速模型参考PI自适应辨识方法,该方法只需检测定子电流和定子电压就能实现异步电机转速的辨识,结构简单、易于实现;然后,采用小信号分析法分析了转速辨识子系统的稳定性;最后,基于Matlab/Simulink仿真平台,搭建异步电机无速度传感器矢量控制系统并进行仿真研究,仿真结果表明:采用基于改进反电动势的转速模型参考PI自适应辨识方法所估计的转速能准确有效地跟踪实际的转速。2、进一步研究了异步电机无速度传感器矢量控制系统的优化控制问题。首先,设计了包含系统转速输出跟踪误差以及调节器输出约束的多性能指标函数,并采用改进的量子免疫遗传算法同时整定异步电机无速度传感器矢量系统中转速调节器、励磁电流调节器、转矩电流调节器以及转速辨识模块中调节器的参数,以实现系统的优化控制;然后,采用Matlab仿真工具,完成异步电机无速度传感器矢量控制系统的优化仿真;最后,基于求是教仪平台,进行实验验证。实验结果表明:采用改进量子免疫遗传算法的优化结果能使调速系统具有良好的性能。
[Abstract]:In the actual speed regulation system of asynchronous motor, the installed speed sensor is easy to be affected by the environment, the cost is increased and it is difficult to maintain, but the speed identification without speed sensor can effectively overcome the above defects. In addition, it is of great significance to optimize the PI- (proportional-integral) regulator parameters in the asynchronous motor speed regulation system. In this paper, the speed identification without speed sensor and the optimization of regulator parameters in vector control system of asynchronous motor are studied in this paper. Firstly, a speed model reference Pi adaptive identification method based on improved backEMF is adopted. The speed identification of asynchronous motor is realized, and the parameters of speed regulator, excitation current regulator, torque current regulator and speed identification module are optimized by using the improved quantum immune genetic algorithm. Finally, in Matlab simulation and hardware platform, the effectiveness of the work is verified. The main work of this paper is as follows: 1. The speed identification problem of asynchronous motor without speed sensor is studied. Firstly, a speed model reference Pi adaptive identification method based on improved backEMF is adopted. This method can identify the speed of asynchronous motor only by detecting stator current and stator voltage, which is simple and easy to realize. The stability of speed identification subsystem is analyzed by small signal analysis method. Finally, based on Matlab/Simulink simulation platform, the speed sensorless vector control system of asynchronous motor is built and simulated. The simulation results show that the speed estimation based on the improved backEMF model reference Pi adaptive identification method can track the actual speed accurately and effectively. Furthermore, the speed sensorless vector of asynchronous motor is studied. Optimal control problem of control system. Firstly, a multi-performance index function including system speed output tracking error and regulator output constraints is designed, and an improved quantum immune genetic algorithm is used to adjust the speed regulator in the speed sensorless vector system of asynchronous motor simultaneously. The parameters of excitation current regulator, torque current regulator and speed identification module are used to realize the optimal control of the system. Then, the optimal simulation of speed sensorless vector control system of asynchronous motor is completed by using Matlab simulation tool. Finally, based on the platform of Qiushi teaching instrument, experimental verification is carried out. The experimental results show that the improved quantum immune genetic algorithm can make the speed regulation system have good performance.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM343

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