压电智能结构自适应滤波振动主动控制研究
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摘要
压电智能结构振动主动控制方法与技术涉及先进材料、控制理论、力学分析、数学建模、科学计算、实验技术等诸多领域，是多学科交叉的前沿课题，具有重要的科研学术意义和工程应用价值。
作为压电智能结构主动减振研究的一个重要发展方向，自适应控制策略成为当前研究的热点，其中自适应滤波控制技术在理论方法和实验验证方面取得了较好的实现效果；但目前面向压电智能结构振动主动控制的自适应滤波控制方法依然存在很多不足，突出体现在控制器设计和算法过程的具体实施适应性上，同时算法缺乏比较完善的系统稳定性及控制效果分析方法，并在工程实现方面也存在较多的问题，需要进一步的深入探索和研究。
本文基于国家自然科学基金科研课题的研究背景，以一种模拟临近空间飞行器压电智能框架结构为实验模型对象，着重进行自适应滤波振动控制方法及其实现算法研究，同时针对模型结构动力学分析方法与压电元件优化配置策略进行积极探索，并在此基础上构建整体实验平台和开发综合测控系统，以验证相关方法技术的可行性和有效性；全文工作可以概括为结构动力学分析与压电元件优化配置、自适应滤波振动控制方法与控制器设计、实验平台构建与实验验证分析三大部分。所做主要工作如下：
（1）以压电智能框架结构为实验对象，将分布式压电元件作为传感器和驱动器粘贴于结构表面，构建了一种模拟临近空间飞行器压电智能框架实验模型结构，并与开发的自适应滤波振动控制系统结合，形成了一套压电智能结构自适应滤波振动主动控制实验系统。
（2）采用行波分析法，对压电智能结构进行动力学分析，同时引入智能结构振动模态有限元分析技术，结合压电智能结构振动特性，分析了压电传感器/作动器的位置优化问题，并以框架组成单元梁为研究对象，给出优化目标函数，引入粒子群优化方法针对目标函数进行优化，实现分布式压电传感器/作动器元件的优化配置。
（3）鉴于经典自适应滤波-X LMS算法过程中需要预知与外激扰信号相关的参考信号，导致该方法存在工程适用性和技术实用性缺陷。本文提出了一种基于滤波-X改进型的参考信号自提取振动控制算法，着重考虑通过从振动结构中直接提取振动响应残差信号，进而基于控制器结构和算法过程数据构造出参考信号，满足与激扰信号的相关性并进入算法控制过程；仿真分析和实验验证表明：所提出改进的控制算法控制效果良好，不仅实现了参考信号的振动结构直接提取策略，并具有较快的收敛速度和良好的控制效果。
（4）在经典的自适应滤波-X LMS算法实施过程中，存在控制通道模型参数辨识问题，一般可采用离线辨识策略获得控制通道模型参数，但也很大程度上导致该方法在工程实际应用时具有较大的不实现性。本文提出一种控制通道模型在线辨识的自适应滤波振动主动控制方法，其基本思想是在控制输出端引入一个随机噪声信号，采用FIR滤波器作为受控通道模型进行实时在线辨识，同时控制环节采用滤波-X控制算法。经过仿真分析和实验验证表明，本文所提的在线辨识控制器设计方法及其实现算法控制效果良好，为进一步深入实用化研究奠定了基础。
（5）由于滤波-X控制器的传输函数是一个全零点的结构，其不考虑控制输出信号的反馈对参考信号的影响，而在实际的系统中这种影响是不能忽略的。滤波-U结构的传输函数中含有零极点，它可以在一定程度上解决振动反馈可能带来的控制系统的不稳定问题。本文以滤波-U为基础结构，分别研究了参考信号自提和控制通道在线辨识问题，分别提出了基于滤波-U的参考信号自提取振动控制方法和控制通道在线辨识的振动控制方法。经过仿真分析和实验验证表明，所提控制方法及其实现算法控制效果良好。
（6）在完成压电智能框架结构振动主动控制平台构建，以及结构振动控制系统软件开发的基础上，对本文所研究的自适应滤波振动控制方法及其实现算法进行了实验分析与验证工作。方法研究与实验验证表明，多通道自适应滤波结构振动控制方法具有较强的自适应能力，能够较快地跟踪受控结构系统参数及外扰响应的变化；本文所提出的自适应滤波振动控制算法与经典滤波-X、滤波-U算法相比，虽然收敛速度略慢，但控制效果良好，尤其为提高自适应滤波控制方法的技术实用性和工程适用性，提供了有益的技术方法思路。
Piezoelectric smart structure active vibration control methods and techniques isan interdisciplinary forefront research topic, which is involved in advanced materials,control theory, mechanical analysis, mathematical modeling, scientific computing,experimental technology, et al. And it has important scientific and academicsignificance and project application value.
As an important research development direction for piezoelectric smartstructure active vibration control, adaptive control strategy is a current researchfocus. Though great progresses have been made for adaptive filter control technology,more research is needed for engineering realization and specific implementation ofadaptive controller design and algorithm process, as well as system stability andcontrol performance analysis.
The research in this dissertation is based on National Natural ScienceFoundation research project. Taking a piezoelectric smart frame structure, whichsimulates model near space vehicle, as the experimental model object, thisdissertation is focused on adaptive filter vibration control methods andimplementation algorithms. Structural dynamics analysis method and optimalpiezoelectric sensors and actuators placement strategy is investigated. Experimentalplatform and integrated measurement and control system is developed to verify thefeasibility and effectiveness of the methods and techniques. The dissertation can besummarized as three parts, structural dynamics analysis and optimal piezoelectricpatches placement, adaptive filter vibration control method and controller design,experimental platform development and experimental validation analysis. Thecontribution of this dissertation is as follows:
(1) Taking piezoelectric smart frame as experiment object, distributedpiezoelectric sensors and actuators are pasted on the structure surface. With theconstructed experimental piezoelectric smart near space vehicle mode, experimentaladaptive filter control system is developed.
(2) Traveling-wave analysis is used for dynamic analysis of piezoelectric smartstructure, while finite element analysis is introduced to analyze the optimalplacement of piezoelectric sensors/actuators. Taking the main component beam units of the framework, the objective target function is given. Particle swarmoptimization method is used to optimize the target function. Optimal placement ofthe distributed piezoelectric sensor/actuators is achieved.
(3) As classical adaptive filtered-X LMS algorithm needs a reference signal,that is related to the excitation signal, there are defects for engineering application.An improved filtered-X algorithm with reference signal extraction is presented byconstructing reference signal from vibration residual signal with the controller itselfdirectly. Simulation analysis and experimental verification shows that the improvedcontrol algorithm, not only realizes reference signal extraction but also has fastconvergence rate and good control performance.
(4) In the implementation of the classical adaptive filter-X LMS algorithm,control channel model parameter identification is very important. Though offlineidentification strategy could gain the channel model parameters, but it is not practicalfor engineering application. An adaptive filter active vibration control method withonline control channel model identification is proposed. The basic idea is tointroduce a random noise signal into the control output, while the FIR filter is usedfor real-time online identification, filtered-X control algorithm is used as the controlalgorithm. Simulation analysis and experimental verification shows that theproposed controller design method and its implementation algorithm have goodcontrol performance, which laid foundation for further practical research.
(5) Since the transfer function of the filtered-X controller is an all-zero structure,it does not consider the feedback of the output signal. But this effect can not beignored in the actual system. As the transfer function of the filtered-U structurecontaining poles and zeros, the instability problem of vibration feedback can besolved to a great extent. Reference signal extraction and online control channelidentification is investigated respectively. And filtered-U vibration control algorithmwith online identification and reference signal extraction is proposed. Simulationanalysis and experimental verification shows that the proposed control method andits implementation algorithm has good control performance.
(6) The structural vibration active control platform for piezoelectric smart frameis developed, as well as control system software. Experimental analysis andverifications for the proposed methods and algorithms are done. Research andexperimental verification shows that the multi-channel adaptive filter vibration control methods have strong adaptive ability, which are able to track the changes ofsystem parameters and external disturbance responses. Although the convergencerate of the proposed adaptive filter vibration control algorithms are somewhatslower, than the classical filtered-X, and filtered-U algorithm, the controlperformance is good. Meanwhile the practicality and engineering applicability of theadaptive filter method is improved by the proposed methods and algorithms.
作为压电智能结构主动减振研究的一个重要发展方向，自适应控制策略成为当前研究的热点，其中自适应滤波控制技术在理论方法和实验验证方面取得了较好的实现效果；但目前面向压电智能结构振动主动控制的自适应滤波控制方法依然存在很多不足，突出体现在控制器设计和算法过程的具体实施适应性上，同时算法缺乏比较完善的系统稳定性及控制效果分析方法，并在工程实现方面也存在较多的问题，需要进一步的深入探索和研究。
本文基于国家自然科学基金科研课题的研究背景，以一种模拟临近空间飞行器压电智能框架结构为实验模型对象，着重进行自适应滤波振动控制方法及其实现算法研究，同时针对模型结构动力学分析方法与压电元件优化配置策略进行积极探索，并在此基础上构建整体实验平台和开发综合测控系统，以验证相关方法技术的可行性和有效性；全文工作可以概括为结构动力学分析与压电元件优化配置、自适应滤波振动控制方法与控制器设计、实验平台构建与实验验证分析三大部分。所做主要工作如下：
（1）以压电智能框架结构为实验对象，将分布式压电元件作为传感器和驱动器粘贴于结构表面，构建了一种模拟临近空间飞行器压电智能框架实验模型结构，并与开发的自适应滤波振动控制系统结合，形成了一套压电智能结构自适应滤波振动主动控制实验系统。
（2）采用行波分析法，对压电智能结构进行动力学分析，同时引入智能结构振动模态有限元分析技术，结合压电智能结构振动特性，分析了压电传感器/作动器的位置优化问题，并以框架组成单元梁为研究对象，给出优化目标函数，引入粒子群优化方法针对目标函数进行优化，实现分布式压电传感器/作动器元件的优化配置。
（3）鉴于经典自适应滤波-X LMS算法过程中需要预知与外激扰信号相关的参考信号，导致该方法存在工程适用性和技术实用性缺陷。本文提出了一种基于滤波-X改进型的参考信号自提取振动控制算法，着重考虑通过从振动结构中直接提取振动响应残差信号，进而基于控制器结构和算法过程数据构造出参考信号，满足与激扰信号的相关性并进入算法控制过程；仿真分析和实验验证表明：所提出改进的控制算法控制效果良好，不仅实现了参考信号的振动结构直接提取策略，并具有较快的收敛速度和良好的控制效果。
（4）在经典的自适应滤波-X LMS算法实施过程中，存在控制通道模型参数辨识问题，一般可采用离线辨识策略获得控制通道模型参数，但也很大程度上导致该方法在工程实际应用时具有较大的不实现性。本文提出一种控制通道模型在线辨识的自适应滤波振动主动控制方法，其基本思想是在控制输出端引入一个随机噪声信号，采用FIR滤波器作为受控通道模型进行实时在线辨识，同时控制环节采用滤波-X控制算法。经过仿真分析和实验验证表明，本文所提的在线辨识控制器设计方法及其实现算法控制效果良好，为进一步深入实用化研究奠定了基础。
（5）由于滤波-X控制器的传输函数是一个全零点的结构，其不考虑控制输出信号的反馈对参考信号的影响，而在实际的系统中这种影响是不能忽略的。滤波-U结构的传输函数中含有零极点，它可以在一定程度上解决振动反馈可能带来的控制系统的不稳定问题。本文以滤波-U为基础结构，分别研究了参考信号自提和控制通道在线辨识问题，分别提出了基于滤波-U的参考信号自提取振动控制方法和控制通道在线辨识的振动控制方法。经过仿真分析和实验验证表明，所提控制方法及其实现算法控制效果良好。
（6）在完成压电智能框架结构振动主动控制平台构建，以及结构振动控制系统软件开发的基础上，对本文所研究的自适应滤波振动控制方法及其实现算法进行了实验分析与验证工作。方法研究与实验验证表明，多通道自适应滤波结构振动控制方法具有较强的自适应能力，能够较快地跟踪受控结构系统参数及外扰响应的变化；本文所提出的自适应滤波振动控制算法与经典滤波-X、滤波-U算法相比，虽然收敛速度略慢，但控制效果良好，尤其为提高自适应滤波控制方法的技术实用性和工程适用性，提供了有益的技术方法思路。
Piezoelectric smart structure active vibration control methods and techniques isan interdisciplinary forefront research topic, which is involved in advanced materials,control theory, mechanical analysis, mathematical modeling, scientific computing,experimental technology, et al. And it has important scientific and academicsignificance and project application value.
As an important research development direction for piezoelectric smartstructure active vibration control, adaptive control strategy is a current researchfocus. Though great progresses have been made for adaptive filter control technology,more research is needed for engineering realization and specific implementation ofadaptive controller design and algorithm process, as well as system stability andcontrol performance analysis.
The research in this dissertation is based on National Natural ScienceFoundation research project. Taking a piezoelectric smart frame structure, whichsimulates model near space vehicle, as the experimental model object, thisdissertation is focused on adaptive filter vibration control methods andimplementation algorithms. Structural dynamics analysis method and optimalpiezoelectric sensors and actuators placement strategy is investigated. Experimentalplatform and integrated measurement and control system is developed to verify thefeasibility and effectiveness of the methods and techniques. The dissertation can besummarized as three parts, structural dynamics analysis and optimal piezoelectricpatches placement, adaptive filter vibration control method and controller design,experimental platform development and experimental validation analysis. Thecontribution of this dissertation is as follows:
(1) Taking piezoelectric smart frame as experiment object, distributedpiezoelectric sensors and actuators are pasted on the structure surface. With theconstructed experimental piezoelectric smart near space vehicle mode, experimentaladaptive filter control system is developed.
(2) Traveling-wave analysis is used for dynamic analysis of piezoelectric smartstructure, while finite element analysis is introduced to analyze the optimalplacement of piezoelectric sensors/actuators. Taking the main component beam units of the framework, the objective target function is given. Particle swarmoptimization method is used to optimize the target function. Optimal placement ofthe distributed piezoelectric sensor/actuators is achieved.
(3) As classical adaptive filtered-X LMS algorithm needs a reference signal,that is related to the excitation signal, there are defects for engineering application.An improved filtered-X algorithm with reference signal extraction is presented byconstructing reference signal from vibration residual signal with the controller itselfdirectly. Simulation analysis and experimental verification shows that the improvedcontrol algorithm, not only realizes reference signal extraction but also has fastconvergence rate and good control performance.
(4) In the implementation of the classical adaptive filter-X LMS algorithm,control channel model parameter identification is very important. Though offlineidentification strategy could gain the channel model parameters, but it is not practicalfor engineering application. An adaptive filter active vibration control method withonline control channel model identification is proposed. The basic idea is tointroduce a random noise signal into the control output, while the FIR filter is usedfor real-time online identification, filtered-X control algorithm is used as the controlalgorithm. Simulation analysis and experimental verification shows that theproposed controller design method and its implementation algorithm have goodcontrol performance, which laid foundation for further practical research.
(5) Since the transfer function of the filtered-X controller is an all-zero structure,it does not consider the feedback of the output signal. But this effect can not beignored in the actual system. As the transfer function of the filtered-U structurecontaining poles and zeros, the instability problem of vibration feedback can besolved to a great extent. Reference signal extraction and online control channelidentification is investigated respectively. And filtered-U vibration control algorithmwith online identification and reference signal extraction is proposed. Simulationanalysis and experimental verification shows that the proposed control method andits implementation algorithm has good control performance.
(6) The structural vibration active control platform for piezoelectric smart frameis developed, as well as control system software. Experimental analysis andverifications for the proposed methods and algorithms are done. Research andexperimental verification shows that the multi-channel adaptive filter vibration control methods have strong adaptive ability, which are able to track the changes ofsystem parameters and external disturbance responses. Although the convergencerate of the proposed adaptive filter vibration control algorithms are somewhatslower, than the classical filtered-X, and filtered-U algorithm, the controlperformance is good. Meanwhile the practicality and engineering applicability of theadaptive filter method is improved by the proposed methods and algorithms.
引文
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