【作者】 马航；

【导师】 杨俊友；

【作者基本信息】 沈阳工业大学 ， 电气工程， 2003， 硕士

【摘要】 本文以国家自然科学基金资助项目《直线伺服双位置环动态精密同步进给理论和实现方法研究(NO．50075057)》为背景，基于神经网络及自适应逆控制理论，提出了直线伺服的高精度快进给的神经逆控制系统方案，并进一步研究了神经网络控制问题。 由于永磁直线同步电动机消除了旋转电机由旋转运动到直线运动的机械传动链的影响，且具有电磁推力强度高、损耗低、电气时间常数小、响应快等特点，使其在高精度、微进给伺服系统中成为执行机构的最佳选择之一。本项目提出了在诸如龙门移动式镗铣床等涉及高精度同步进给技术的现代加工设备中，采用永磁直线同步电机作为同步进给的驱动元件，以发挥其高速的动态响应能力，实现动态同步进给。 传统的矢量控制算法是建立在电动机数学模型的基础上的，系统的控制性能往往受模型的精确程度和电动机的参数影响较大。本论文在人工神经网络控制理论的基础上，针对永磁直线同步电动机矢量控制系统，构造MIMO神经网络PID矢量控制系统。通过大量仿真和实验表明，本系统不仅具有较好的动态和稳态性能，而且避免了系统的数学模型、电动机参数变化等因素的不良影响，有很好的自适应性和鲁棒性。 本文的创新之处在于，将直线电动机作为一个的被控对象，利用自适应逆控制理论设计了一个BP神经网络自适应逆控制系统，以此解决高精度进给的控制问题，并在此基础上，研究了神经逆控制问题来解决模型摄动和参数不确定性给系统带来的扰动，以保证系统的稳定性及鲁棒性；利用逆控制器的快速反应能力来保证系统的快速性，共同实现系统的性能指标。 所提出的控制方案有严格的理论基础，既保证了闭环系统的稳定性和快速的跟随性能，又抑制了模型摄动及外部干扰对系统的影响，保证了系统的鲁棒性能。仿真也验证了所提方案是行之有效的。更多还原

【Abstract】 A new kind of inverse control tactic based on neural network control for dynamical and rapid high-precision-feed in a single axis is proposed in this thesis, The background of the thesis is "Theory of Dynamic Precision synchronization Traverse and Research of Realization Methods for Linear Servo Dual Position Loops System (NO.50075057)", which is supported by National Natural Science Foundation of China.The linear permanent magnet synchronous motor(LPMSM) has avoided the effects of the mechanical transmission chains from rotary motions to linear ones, and has strong electromagneticm thrust, lower cost, small electrical tune constant and rapid response etc., which becomes one of the best executive machines in high-precision and micro-feed servo system. In this project, using LPMSM as driving parts in modern mechanical systems involved high-precision synchronized feed technology such as gantry-moving type milling machine is first proposed so as to bring their high-speed dynamic response ability into playfor realizing dynamically synchronized feedConventional vector control algorithm is dependent on the mathematical model of motor.Thus, the control performance of system is influenced to a great extent by the model precision and the linear parameters of motor. Based on artificial neutral network control theory, a MEMO neutral network PID neural network vector control system realizing the maximum torque with minimum current is conformed for a LPMSM drive system. A number of simulation and experiments show that the system not only has good performance both in dynamic and steady state but also avoids the adverse effect resulted from the inaccurate mathematical model and the parameter changes of the LPMSM. Therefore, the system possesses good self-adapting and robustThe innovative ideas in this thesis are that neural network adaptive inverse control theory based on LPMSM control system is applied to the high-precision feed of a single axis, and the controller is composed of inverse model of linear motor. A neural network inverse control system restrains disturbances and uncertainties to keep the robust and stable performances. Theadaptive inverse control system, which has the ability of rapid response, is applied to satisfy the rapid performance. Both the inner system and external system are used to satisfy the required tracking performances.The proposed control scheme has an adaptive inverse control theoretic and neural network base. The controllers designed not only guarantee the stability robustness and performance robustness of the system but also the tracking performance of the system. The simulation results show that the design is reasonable and effective.更多还原