【作者】 孙晓东；

【导师】 朱熀秋；

【作者基本信息】 江苏大学 ， 控制理论与控制工程， 2008， 硕士

【摘要】 针对无轴承异步电机是一个强耦合、非线性多变量复杂系统,本文在国家自然科学基金(60674095,50575099)的资助下,将神经网络与逆系统方法结合,提出了无轴承异步电机的神经网络逆解耦控制方法。该方法可有效地克服无轴承异步电机的未建模动态、参数变化及负载扰动对控制性能的影响,真正实现无轴承异步电机的线性化动态解耦控制。本文的具体内容如下:首先,介绍了无轴承异步电机径向悬浮力的产生原理,推导了无轴承异步电机的数学模型。针对电机电磁转矩和径向悬浮力之间的耦合特性,分析了无轴承异步电机的转子磁场定向和气隙磁场定向矢量控制系统,利用Matlab/Simulink工具箱对两种控制系统进行了仿真,仿真试验表明两个控制系统基本实现了径向悬浮力和旋转力矩之间的稳态解耦,并根据仿真结果对两种磁场定向控制进行了比较分析。其次,针对无轴承异步电机矢量控制只能进行稳态解耦的局限,提出了应用神经网络逆系统控制理论策略对其进行动态解耦控制研究,不仅成功的实现了径向位移子系统和转矩(转速)子系统之间无耦合,而且使各个被控量变换成具有线性传递关系,系统设计得以简化,并采用线性系统理论进行了综合和仿真试验。最后,基于对无轴承异步电机的神经网络逆解耦控制策略的分析,应用TMS320LF2407A DSP构建了数字控制系统的硬件,开发了数字控制系统的软件,给出了各个功能模块的流程图。并以二自由度无轴承异步电机为实验样机,对数字控制系统进行调试和优化,给出了相关实验结果,并进行了分析,通过实验来验证控制系统和控制方法的正确性。更多还原

【Abstract】 Sponsored by the National Natural Science Foundation of China, this dissertation focuses on the fact that the bearingless induction motor is a strong-coupled, nonlinear, multi-variable complicated system. Combined neural networks with inverse system mothod, the neural network inverse system method for bearingless induction motor control is proposed. The influence caused by unmodeling dynamics, parameter variation and load disturbance is decreased evidently, and the bearingless induction motor is linearized and decoupled into four SISO subsystems. Main contents of this dissertation are as follows:Firstly, the principle of radial suspension force is expounded. The mathematics models of radial suspension forces and rotation part of the bearingless induction motor are deduced. In order to realize the decoupling control of torque and radial suspension forces for bearingless induction motor, two control systems based on rotor flux oriented control and air-gap flux oriented control are designed. The two control systems are simulated with Matlab/Simulink toolbox. Simulation results have shown that the static decoupling between torque and radial suspension are achieved basically. According to the simulation results, the rotor flux oriented control and air-gap flux oriented control are compared and analyzed.Secondly, according to the vector control only can realize static decoupling for the bearingless induction motor, a method based on neural network inverse system method has been used successfully in realizing dynamic decoupling control among radial displacement subsystems and torque subsystem. And this method is realized that each subsystems not only have no coupling, but also all subsystems have been linearized, therefore we design the system and attain the ideal performance easily. Then, linear control system techniques are applied to these linearization subsystems to synthesize and simulate.Finally, based on the principle of neural network inverse system method, the digital control system is designed using TMS320LF2407A of TI Corporation, and the flowcharts of each functional block are presented. Based on the prototype of bearingless induction motor, the digital control system is debugged and optimized. The experimental rig of digital control system is set up and is used to validate control strategy.更多还原