【作者】 周童；

【导师】 刘国海；

【作者基本信息】 江苏大学 ， 电力电子与电力传动， 2009， 硕士

【摘要】 永磁同步电机是当今众多高精度、高效、节能自动化控制系统的驱动机构的核心执行部件。对永磁同步电机的控制策略进行改良可以直接改善这些自动化设备的工作性能,达到提高产品性能,节能降耗的目的。本文针对矢量控制等依赖于对象精确模型的永磁同步电机高性能控制方法中存在的问题(如只能实现稳态解耦,鲁棒性和适应性差等),将神经网络与逆系统方法结合,提出了永磁同步电机的神经网络逆解耦控制方法。该方法能减小电机参数时变、负载扰动以及未建模动态对电机解耦控制的影响,真正实现永磁同步电机的线性化动态解耦控制,有效地提高永磁同步电机的控制性能。论文主要进行了以下的研究工作:首先,应用非线性系统的线性代数方法,给出了一般非线性系统的静态逆系统和动态逆系统的构造算法,说明了非线性系统可逆性的充要条件以及伪线性系统复合构造的方法,从理论上阐述了逆系统的线性化解耦控制策略。然后,全面、系统地分析了永磁同步电机的可逆性及其逆系统的线性化解耦特征,给出了永磁同步电机的状态反馈结合输入积分逆系统的解析表达式,并通过仿真实验分析了参数变化及负载扰动对解析逆解耦控制产生的严重影响。得出了永磁同步电机的解析逆系统方法在实际中难以应用的结论。为解决由于永磁同步电机因受参数变化和负载扰动影响而使解析逆系统方法难以在实际中应用的难题,提出了永磁同步电机的神经网络逆解耦控制方法。给出了永磁同步电机神经网络逆模型的结构形式、辨识方法及实现步骤,将永磁同步电机这样一个多变量、强耦合、时变的非线性对象动态解耦成两个SISO的伪线性积分子系统。之后,进一步对两个积分子系统分别设计闭环线性控制器,使整个系统具有很强的参数鲁棒性和良好的抗负载扰动的能力,为逆系统方法在高性能永磁同步电机控制中的应用莫定了基础。最后,在以IPM为功率变换核心,DSP为数字化控制单元的电机控制实验平台上实现了永磁同步电机神经网络逆解耦控制实验。控制系统具有良好的动、静态性能。实验结果表明永磁同步电机的神经网络逆解耦控制方法是一种有效和实用的高性能控制方法。更多还原

【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.更多还原