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Research on Parameter Identification of Modified Friction LuGre Model Based Distributions Theory

机译:基于分布理论的改进摩擦LuGre模型参数辨识研究

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摘要

Based on distributions theory to continuous time systems with friction using the this paper presents a batch on-line method for the parameter identification of the modified LuGre friction model. Mainly it applies the results of the identification procedures based on distributions theory to continuous time systems with friction. There are defined the so called generalized friction dynamic systems (GFDS) as a closed loop structure around a smooth system with discontinuous feedback loops representing friction reaction vectors. Both GFDS with static friction models (SFM) and dynamic friction models (DFM), also the modified friction LuGre model is analyzed. The identification problem is formulated as a condition of vanishing the existence relation of the system. Then, this relation is represented by functional using techniques from distribution theory based on testing function from a finite dimensional fundamental space. The advantage of representing information by distributions are pointed out when special evolutions as sliding mode, or limit cycle can appear. The proposed method is a batch on-line identification because identification results are obtained during the system evolution after some time intervals but not in any time moment. This method does not require the derivatives of measured signals for its implementation. Some experimental results are presented to illuminate its advantages and practical use. At last, the simulation results have shown effectiveness of the proposed method for friction parameter identification.
机译:本文基于具有摩擦的连续时间系统的分布理论,提出了一种改进的LuGre摩擦模型参数识别的批处理在线方法。主要将基于分布理论的识别过程的结果应用于具有摩擦的连续时间系统。所谓的广义摩擦动力系统(GFDS)被定义为围绕光滑系统的闭环结构,该系统具有代表摩擦反应矢量的不连续反馈回路。分析了带有静摩擦模型(SFM)和动摩擦模型(DFM)的GFDS,以及改进的LuGre摩擦模型。识别问题被表述为消除系统存在关系的条件。然后,这种关系通过基于分布理论的技术使用函数来表示,该技术基于有限维基本空间的测试函数。当出现特殊变化(如滑模或极限环)时,指出了用分布表示信息的优势。所提出的方法是批量在线识别,因为识别结果是在系统演化过程中经过一定时间间隔后获得的,而不是在任何时间点内获得的。该方法不需要测量信号的导数即可实现。提出了一些实验结果以阐明其优点和实际应用。最后,仿真结果表明了该方法在摩擦参数识别中的有效性。

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