永磁同步伺服电机控制策略的仿真及对比研究

发布时间：2018-06-28 10:52

本文选题：伺服电机 + PID控制　；参考：《哈尔滨理工大学》2014年硕士论文

【摘要】：伺服电机在工业领域主要用于控制机械元件的运转，可实现准确的速度、位置控制。目前交流伺服电机已经取代直流电机，成为了伺服系统的主流，而永磁同步伺服电机(PMSM)因为其优越的性能得到了愈加广泛的应用。永磁同步伺服电机的控制策略直接影响了整个伺服系统的性能，对PMSM的控制一直以来都是研究的重点。但是由于实际的伺服系统中存在着不确定性以及外部干扰、内部扰动，一般的PID控制难以满足控制要求，近年来神经网络控制、滑模变结构控制等控制策略的研究成为热点，以更好的提高系统的工作性能。本文建立了PMSM的数学模型，运用增量式PID与积分可分离结合起来的方法对PMSM的常规PID控制策略进行了仿真分析。随后借助神经网络的学习能力，去调节PID控制器的参数，进一步改善了PID控制器的性能。滑模变结构控制在控制性能上优于常规PID控制，该控制策略能够使系统稳定的工作在滑模面，进一步提高系统的稳定性。由于小脑模型关节控制器(CMAC)是神经网络的一个分支，其具有快速的学习能力，为了使系统更加稳定，构造了基于CMAC网络的滑模控制器，使用CMAC网络去补偿滑模控制，达到加快收敛速度的目的。经过仿真分析，分别得到上述各种控制策略的控制结果，可知神经网络能够提高提高PID控制器以及滑模控制器的性能。并且把基于CMAC网络的滑模控制与常规的PID控制算法、神经网络控制PID以及滑模变结构控制进行仿真对比分析，可以清楚的看到基于CMAC网络滑模控制的系统无论是在收敛速度还是在跟踪精度方面均能得到了明显的改善和加强。
[Abstract]:Servo motor is mainly used to control the operation of mechanical components in the industrial field, which can achieve accurate speed and position control. At present AC servo motor has replaced DC motor and become the mainstream of servo system. Permanent magnet synchronous servo motor (PMSM) has been more and more widely used because of its superior performance. The control strategy of PMSM has a direct impact on the performance of the whole servo system. The control of PMSM has always been the focus of research. However, due to the uncertainty and external disturbance and internal disturbance in the actual servo system, the general pid control is difficult to meet the control requirements. In recent years, neural network control, sliding mode variable structure control and other control strategies have become a hot topic. To better improve the performance of the system. In this paper, the mathematical model of PMSM is established, and the conventional pid control strategy of PMSM is simulated and analyzed by combining incremental pid and integral. Then the parameters of pid controller are adjusted by the learning ability of neural network, and the performance of pid controller is further improved. The sliding mode variable structure control is superior to the conventional pid control in control performance. The control strategy can make the system work stably on the sliding mode surface and further improve the stability of the system. Because the cerebellar model joint controller (CMAC) is a branch of neural network, it has fast learning ability. In order to make the system more stable, a sliding mode controller based on CMAC network is constructed, and CMAC network is used to compensate sliding mode control. To speed up convergence. The simulation results show that the neural network can improve the performance of pid controller and sliding mode controller. And the sliding mode control based on CMAC network is compared with the conventional pid control algorithm, neural network control pid and sliding mode variable structure control. It can be clearly seen that the system based on CMAC network sliding-mode control can be improved and strengthened obviously in terms of convergence speed and tracking accuracy.
【学位授予单位】：哈尔滨理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM383.4

【引证文献】