基于磁致伸缩平台的微振动主动控制研究
发布时间：2018-11-03 14:27
【摘要】：在精密工程及航天工程领域，制造、检测过程的内外部扰动严重影响产品质量或检测质量，因此有必要对设备所受的微振动干扰进行隔离和控制。为了解决这类微振动问题，本论文以巨精密磁致伸缩平台为研究对象，通过引入自适应控制算法对平台系统进行精密位形驱动和微振动控制，进行了一系列仿真与实验测试。 论文的主要研究工作如下： （1）本文首先以单个巨磁致伸缩驱动器（GMA）为研究对象，通过受控自回归滑动平均模型(CARMA)与递推增广最小二乘法相结合来对巨磁致伸缩驱动器实现在线模型辨识，辨识模型能精确描述GMA输出位移，辨识误差达0.23%，与Prandtl Ishlinskii模型相比，CARMA模型辨识速度更快且精度更高；将改进的广义预测控制算法（MGPC）应用于GMA的闭环位移控制，与其它算法相比，MGPC具有更好的实时性和更高的控制精度，在0-10um给定位移下，其驱动控制误差达0.143um。 （2）建立巨磁致伸缩驱动器的动力学模型，阐述其具有可调刚度特性。通过分析比较GMA与被动隔振器不同连接方式对系统隔振性能的影响，设计基于主被动隔振器串联结构的隔振系统。最后应用上述CARMA模型和MGPC算法对GMA隔振系统进行微振动控制仿真研究和闭环实验测试，抑制效果达到20dB。 （3）介绍了三自由度磁致伸缩平台的全系统结构，建立系统动力学模型，之后进行仿真与开环测试研究，分析比较模型的准确性。将本文研究的基于CARMA模型的改进广义预测自适应控制算法应用于三自由度全系统的位形驱动控制和微振动控制，，仿真与实验结果表明，该控制算法对于三自由度磁致伸缩平台也同样可以达到良好的控制精度和隔振效果。
[Abstract]:In the field of precision engineering and aerospace engineering, the internal and external disturbances of manufacturing and testing process seriously affect the quality of products or the quality of detection, so it is necessary to isolate and control the micro-vibration interference of equipment. In order to solve this kind of micro-vibration problem, a series of simulation and experimental tests are carried out by introducing adaptive control algorithm to the precise configuration drive and micro-vibration control of the platform system. The main work of this thesis is as follows: (1) in this thesis, a single giant magnetostrictive actuator (GMA) is firstly studied. The on-line model identification of giant magnetostrictive actuator is realized by combining the controlled autoregressive moving average model (CARMA) with the recursive augmented least square method. The identification model can accurately describe the output displacement of GMA, and the identification error is 0.23. Compared with Prandtl Ishlinskii model, CARMA model is faster and more accurate. The improved generalized predictive control algorithm (MGPC) is applied to the closed-loop displacement control of GMA. Compared with other algorithms, MGPC has better real-time performance and higher control precision. Under the given displacement of 0-10um, the driving control error is 0.143 um. (2) the dynamic model of giant magnetostrictive actuator is established and its adjustable stiffness is described. By analyzing and comparing the influence of different connection modes between GMA and passive isolator on the isolation performance of the system, a vibration isolation system based on the series structure of active and passive vibration isolators is designed. Finally, the microvibration control simulation and closed-loop test of GMA isolation system are carried out by using the CARMA model and MGPC algorithm, and the suppression effect is up to 20 dB. (3) the whole system structure of 3-DOF magnetostrictive platform is introduced, the dynamic model of the system is established, and then the simulation and open-loop test are carried out, and the accuracy of the model is analyzed and compared. The improved generalized predictive adaptive control algorithm based on CARMA model is applied to the configuration drive control and micro-vibration control of the three-degree-of-freedom system. The simulation and experimental results show that, The control algorithm can also achieve good control accuracy and vibration isolation for the three-DOF magnetostrictive platform.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TB535
[Abstract]:In the field of precision engineering and aerospace engineering, the internal and external disturbances of manufacturing and testing process seriously affect the quality of products or the quality of detection, so it is necessary to isolate and control the micro-vibration interference of equipment. In order to solve this kind of micro-vibration problem, a series of simulation and experimental tests are carried out by introducing adaptive control algorithm to the precise configuration drive and micro-vibration control of the platform system. The main work of this thesis is as follows: (1) in this thesis, a single giant magnetostrictive actuator (GMA) is firstly studied. The on-line model identification of giant magnetostrictive actuator is realized by combining the controlled autoregressive moving average model (CARMA) with the recursive augmented least square method. The identification model can accurately describe the output displacement of GMA, and the identification error is 0.23. Compared with Prandtl Ishlinskii model, CARMA model is faster and more accurate. The improved generalized predictive control algorithm (MGPC) is applied to the closed-loop displacement control of GMA. Compared with other algorithms, MGPC has better real-time performance and higher control precision. Under the given displacement of 0-10um, the driving control error is 0.143 um. (2) the dynamic model of giant magnetostrictive actuator is established and its adjustable stiffness is described. By analyzing and comparing the influence of different connection modes between GMA and passive isolator on the isolation performance of the system, a vibration isolation system based on the series structure of active and passive vibration isolators is designed. Finally, the microvibration control simulation and closed-loop test of GMA isolation system are carried out by using the CARMA model and MGPC algorithm, and the suppression effect is up to 20 dB. (3) the whole system structure of 3-DOF magnetostrictive platform is introduced, the dynamic model of the system is established, and then the simulation and open-loop test are carried out, and the accuracy of the model is analyzed and compared. The improved generalized predictive adaptive control algorithm based on CARMA model is applied to the configuration drive control and micro-vibration control of the three-degree-of-freedom system. The simulation and experimental results show that, The control algorithm can also achieve good control accuracy and vibration isolation for the three-DOF magnetostrictive platform.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TB535
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