精密超磁致伸缩微位移驱动智能构件技术研究
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摘要
异形孔结构能够显著提高活塞的使用寿命,但由于异形孔结构的特殊性,给异形孔的加工提出了难题。为了解决活塞异形孔加工这一难题,本文研究了一种新的加工方法-超磁致伸缩材料(Giant Magnetostrictive Material,GMM)智能镗杆构件。深入系统地研究了GMM智能镗杆构件的设计及精密位移控制等若干关键技术,如:GMM智能镗杆构件的电-磁-机三场耦合有限元分析模型、与电-磁-机三场耦合有限元模型相结合的多目标优化设计方法、温度控制方法及其流-热耦合温度分布有限元分析模型、迟滞非线性建模和控制方法、温度对GMM智能镗杆构件性能的影响,建立了GMM智能镗杆构件测试平台。
第1章介绍加工活塞异形孔微位移驱动机构研究现状,在分析GMM执行器(GMA)有限元建模、优化设计、热误差消除策略及迟滞非线性控制基础上,指出目前GMA设计研究中存在的一些问题,从而确定本文的研究内容和选题意义。
第2章在分析目前GMM有限元模型研究不足的基础上,提出了适合工程设计需要的GMM智能镗杆构件的电-磁-机三场耦合分步有限元模型。为了减少求解自由度,将GMM电-磁-机耦合分解为:首先求解载流线圈产生磁场,然后求解GMM磁-机耦合。通过理论分析得出载流线圈的电-磁、GMM的磁-机耦合场弱解形式的有限元公式。模型在COMSOL 3.4中实施,分析设计GMM智能镗杆构件的静态变形和固有频率及频率响应。
第3章根据设计GMM智能镗杆构件的结构,提出了基于GMM电-磁-机三场耦合有限元的GMM智能镗杆构件多目标优化模型。模型优化目标为:GMM智能镗杆构件最大弯曲变形、一阶固有频率、GMM镗杆构件驱动线圈的电感、驱动线圈的响应时间常数和驱动线圈的效率常数。深入研究了多目标遗传算法NSGA-Ⅱ,并应用NSGA-Ⅱ实现了上述优化模型。根据优化结果,设计了GMM智能镗杆构件。
第4章针对目前各种GMA热误差消除方法的特点,提出了GMM智能镗杆构件温度控制方法-简化强制水冷温度控制,并给出了具体控制方案。在分析GMM智能镗杆构件强制水冷特点基础上,建立了GMM智能镗杆构件流-热耦合有限元分析模型。应用此模型研究分析了所设计温控系统,根据分析结果,设计了GMM智能镗杆构件温度控制系统。
第5章在深入分析迟滞非线性系统建模基础上,通过将智能镗杆构件的输出位移及其变化率作为网络的输入,镗杆构件的输入驱动电流作为输出,使GMM迟滞输入和输出之间一对多映射关系转化为一对一。在此基础上,提出了迟滞非线性系统的CMAC神经网络建模方法,并应用该CMAC网络建立了GMM智能镗杆构件的迟滞非线性模型。提出了基于CMAC和PD反馈相结合的GMM智能镗杆构件控制方法,仿真结果表明所提控制方法能实现GMM智能镗杆构件精密位移跟踪控制。
第6章建立了基于虚拟仪器的GMM智能镗杆构件综合实验平台。实验研究智能镗杆构件静态刚度、静态电流位移关系、输入电流和磁场关系、驱动磁场和位移关系。实验分别研究了温升对GMM智能镗杆构件电流位移关系、电流和驱动磁场以及驱动磁场和弯曲量关系的影响。本章的研究为提高GMM智能镗杆构件性能提供技术支持。
第7章概括全文的主要研究工作,并对将来的研究工作作了展望。
The non-circular hole of a piston can remarkable increase its useful life. Due to the particularity of non-circular hole of a piston, the problems for machining non-circular hole appear. In order to solve the difficult problems of precise machining a non-cylinder pin hole of a piston, a new machining method is presented using embedded giant magnetostrictive material (GMM) in the component. Some key technologies of GMM smart component design and precision position control are studied in depth in this dissertation, such as: the GMM smart component coupled electric, magnetic and mechanical fields finite element model suitable for engineering use, multi-objective optimization integrated above mentioned FE model method, temperature control strategy, coupled fluid and thermal fields FE model, hysteresis nonlinear modeling, control strategy eliminating hysteresis and temperature rising effects on the performance of GMM smart component. The GMM smart component test platform is constructed.
In chapter 1, research on micro-displacement mechanism for machining noncircular hole of a piston is stated.Based on analysis on the GMA FEM modeling, optimization design, eliminating thermal error strategy and hysteresis nonlinear control, the problems are proposed in the GMA design, and then the main content of this dissertation and the project significance are proposed.
In chapter 2, on the basis of the deficiencies of GMM FE analysis model at the present time, the coupled electric, magnetic and mechanical fields fractional step model suitable for engineering use is proposed. To reduce the freedom of node needed to solved, firstly the magnetic field of coil is solved, secondly the coupled magnetic and mechanical fields are calculated. According to Hamilton's principle, the finite element weak form formulations for purely magnetic field of coil and coupled magnetic and mechanical fields of the GMM are derived. The proposed model is implemented by using COMSOL Multiphysics 3.4. The effects on the GMM smart component designed deformation and the system resonance frequencies are studied.
In chapter 3, the smart component multi-objective optimization model integrated the GMM electric, magnetic and mechanical fields FE model GMM is proposed. The optimum objects are the deformation of GMM smart component's top under 3 A input current, the first nature frequency, the inductance of driving coil, the responsive time constant and the energy efficiency constant of driving coil. On the basis of analysis of the multi-objective genetic algorithm NSGA-II in depth, the above mentioned optimization model is implemented using NSGA-II. According to the optimum results, the GMM smart component is designed.
In chapter 4, base on the various measures for eliminating thermal effects on GMA, the reduced forced water cool control strategy is proposed for the giant magnetostrictive smart component and control schemes is provided in detail too. Through the characterizations of forced water cool analysis, a coupled fluid-thermal field finite element model is constructed. The model constructed is used to analyze the GMM smart component temperature distribution. The temperature control system for GMM smart component is constructed on the basis of the FE analysis.
In chapter 5, on the basis of analysis on hysteresis nonlinear modeling, the input datas of neural network are the current smart component output and the output rate, the output of neural network is smart component input. Thus, the mathematical relation between these two outputs of smart component and its current input becomes an one-to-one mapping, which guarantees the complex rate-dependent inverse hysteresis model can be approximated by the neural network. The CMAC modeling method for hysteresis is proposed. The GMM smart component hysteresis model is constructed using this CMAC. A real-time hysteretic compensation control strategy combining a CMAC neural network feed forward controller and a proportional derivative (PD) feedback controller is proposed to implement the precision position tracking control of the smart component. Simulation shows that this control strategy can on-line obtain inverse hysteresis model of the smart component, eliminate the hysteretic nonlinear impact and achieve the precision control of the smart component.
In chapter 6, the experimental table for GMM smart component based on the virtual instrument technologies is built up. The GMM smart component static stiffness, the relationships between input current and displacement, between input current and driving magnetic field, between driving magnetic field and displacement are studied through experiment, respectively. At various temperatures, the GMM smart component relationships between input current and displacement, between input current and driving magnetic field, between driving magnetic field and displacement are studied through experiment, respectively. Through this part's research efforts, the performance of GMM smart component is learned more. So these works can provide technical assistance for GMM smart component.
In chapter 7, the main conclusions of this dissertation are summarized and the future research work is put forward.
第1章介绍加工活塞异形孔微位移驱动机构研究现状,在分析GMM执行器(GMA)有限元建模、优化设计、热误差消除策略及迟滞非线性控制基础上,指出目前GMA设计研究中存在的一些问题,从而确定本文的研究内容和选题意义。
第2章在分析目前GMM有限元模型研究不足的基础上,提出了适合工程设计需要的GMM智能镗杆构件的电-磁-机三场耦合分步有限元模型。为了减少求解自由度,将GMM电-磁-机耦合分解为:首先求解载流线圈产生磁场,然后求解GMM磁-机耦合。通过理论分析得出载流线圈的电-磁、GMM的磁-机耦合场弱解形式的有限元公式。模型在COMSOL 3.4中实施,分析设计GMM智能镗杆构件的静态变形和固有频率及频率响应。
第3章根据设计GMM智能镗杆构件的结构,提出了基于GMM电-磁-机三场耦合有限元的GMM智能镗杆构件多目标优化模型。模型优化目标为:GMM智能镗杆构件最大弯曲变形、一阶固有频率、GMM镗杆构件驱动线圈的电感、驱动线圈的响应时间常数和驱动线圈的效率常数。深入研究了多目标遗传算法NSGA-Ⅱ,并应用NSGA-Ⅱ实现了上述优化模型。根据优化结果,设计了GMM智能镗杆构件。
第4章针对目前各种GMA热误差消除方法的特点,提出了GMM智能镗杆构件温度控制方法-简化强制水冷温度控制,并给出了具体控制方案。在分析GMM智能镗杆构件强制水冷特点基础上,建立了GMM智能镗杆构件流-热耦合有限元分析模型。应用此模型研究分析了所设计温控系统,根据分析结果,设计了GMM智能镗杆构件温度控制系统。
第5章在深入分析迟滞非线性系统建模基础上,通过将智能镗杆构件的输出位移及其变化率作为网络的输入,镗杆构件的输入驱动电流作为输出,使GMM迟滞输入和输出之间一对多映射关系转化为一对一。在此基础上,提出了迟滞非线性系统的CMAC神经网络建模方法,并应用该CMAC网络建立了GMM智能镗杆构件的迟滞非线性模型。提出了基于CMAC和PD反馈相结合的GMM智能镗杆构件控制方法,仿真结果表明所提控制方法能实现GMM智能镗杆构件精密位移跟踪控制。
第6章建立了基于虚拟仪器的GMM智能镗杆构件综合实验平台。实验研究智能镗杆构件静态刚度、静态电流位移关系、输入电流和磁场关系、驱动磁场和位移关系。实验分别研究了温升对GMM智能镗杆构件电流位移关系、电流和驱动磁场以及驱动磁场和弯曲量关系的影响。本章的研究为提高GMM智能镗杆构件性能提供技术支持。
第7章概括全文的主要研究工作,并对将来的研究工作作了展望。
The non-circular hole of a piston can remarkable increase its useful life. Due to the particularity of non-circular hole of a piston, the problems for machining non-circular hole appear. In order to solve the difficult problems of precise machining a non-cylinder pin hole of a piston, a new machining method is presented using embedded giant magnetostrictive material (GMM) in the component. Some key technologies of GMM smart component design and precision position control are studied in depth in this dissertation, such as: the GMM smart component coupled electric, magnetic and mechanical fields finite element model suitable for engineering use, multi-objective optimization integrated above mentioned FE model method, temperature control strategy, coupled fluid and thermal fields FE model, hysteresis nonlinear modeling, control strategy eliminating hysteresis and temperature rising effects on the performance of GMM smart component. The GMM smart component test platform is constructed.
In chapter 1, research on micro-displacement mechanism for machining noncircular hole of a piston is stated.Based on analysis on the GMA FEM modeling, optimization design, eliminating thermal error strategy and hysteresis nonlinear control, the problems are proposed in the GMA design, and then the main content of this dissertation and the project significance are proposed.
In chapter 2, on the basis of the deficiencies of GMM FE analysis model at the present time, the coupled electric, magnetic and mechanical fields fractional step model suitable for engineering use is proposed. To reduce the freedom of node needed to solved, firstly the magnetic field of coil is solved, secondly the coupled magnetic and mechanical fields are calculated. According to Hamilton's principle, the finite element weak form formulations for purely magnetic field of coil and coupled magnetic and mechanical fields of the GMM are derived. The proposed model is implemented by using COMSOL Multiphysics 3.4. The effects on the GMM smart component designed deformation and the system resonance frequencies are studied.
In chapter 3, the smart component multi-objective optimization model integrated the GMM electric, magnetic and mechanical fields FE model GMM is proposed. The optimum objects are the deformation of GMM smart component's top under 3 A input current, the first nature frequency, the inductance of driving coil, the responsive time constant and the energy efficiency constant of driving coil. On the basis of analysis of the multi-objective genetic algorithm NSGA-II in depth, the above mentioned optimization model is implemented using NSGA-II. According to the optimum results, the GMM smart component is designed.
In chapter 4, base on the various measures for eliminating thermal effects on GMA, the reduced forced water cool control strategy is proposed for the giant magnetostrictive smart component and control schemes is provided in detail too. Through the characterizations of forced water cool analysis, a coupled fluid-thermal field finite element model is constructed. The model constructed is used to analyze the GMM smart component temperature distribution. The temperature control system for GMM smart component is constructed on the basis of the FE analysis.
In chapter 5, on the basis of analysis on hysteresis nonlinear modeling, the input datas of neural network are the current smart component output and the output rate, the output of neural network is smart component input. Thus, the mathematical relation between these two outputs of smart component and its current input becomes an one-to-one mapping, which guarantees the complex rate-dependent inverse hysteresis model can be approximated by the neural network. The CMAC modeling method for hysteresis is proposed. The GMM smart component hysteresis model is constructed using this CMAC. A real-time hysteretic compensation control strategy combining a CMAC neural network feed forward controller and a proportional derivative (PD) feedback controller is proposed to implement the precision position tracking control of the smart component. Simulation shows that this control strategy can on-line obtain inverse hysteresis model of the smart component, eliminate the hysteretic nonlinear impact and achieve the precision control of the smart component.
In chapter 6, the experimental table for GMM smart component based on the virtual instrument technologies is built up. The GMM smart component static stiffness, the relationships between input current and displacement, between input current and driving magnetic field, between driving magnetic field and displacement are studied through experiment, respectively. At various temperatures, the GMM smart component relationships between input current and displacement, between input current and driving magnetic field, between driving magnetic field and displacement are studied through experiment, respectively. Through this part's research efforts, the performance of GMM smart component is learned more. So these works can provide technical assistance for GMM smart component.
In chapter 7, the main conclusions of this dissertation are summarized and the future research work is put forward.
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