基于电液位置伺服系统机床滑台模糊自适应控制研究

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英文篇名：Study on Fuzzy Adaptive Control of Machine Tool Sliding Table with Electro-hydraulic Position Servo System 作者：司昌练 英文作者：SI Chang-lian;Shanxi Polytechnic Institute; 关键词：组合机床 ; 电液伺服 ; 模糊自适应 ; AMESim-MATLAB 英文关键词：combination machine tool;;electro-hydraulic servo;;fuzzy adaptive;;AMESim-MATLAB 中文刊名：YYQD 英文刊名：Hydraulics Pneumatics & Seals 机构：陕西工业职业技术学院; 出版日期：2019-05-15 出版单位：液压气动与密封 年：2019 期：v.39;No.251 基金：国家科技重大专项资助项目(2017SX02042-012) 语种：中文; 页：YYQD201905007 页数：6 CN：05 ISSN：11-4839/TH 分类号：37-42

摘要

针对传统电液位置伺服液压机床系统的非线性、强耦合、稳定性低等问题,提出了一种改进模糊自适应控制算法,使得机床滑台位置根据外界条件能够实时跟踪以达到稳定控制。首先,以华中数控组合机床为实例,对机床整体结构及功能进行介绍;然后,建立机床滑台控制模型、传递函数,并设计改进后的模糊自适应控制器;最后,结合AMESim-MATLAB软件搭建电液位置伺服系统滑台模糊自适应控制模型,并对搭建好的模型进行联合实验仿真和实际系统测试。测试实验结果表明,基于电液位置伺服系统的机床滑台模糊自适应算法相对于传统PID算法的位置跟踪效果好,位置控制响应速度快、误差小、鲁棒性强,验证了改进后的模糊自适应控制算法的可行性。
        Aiming at the problems of non-linearity, strong coupling and low stability of traditional electro-hydraulic position servo hydraulic machine tool system, an improved fuzzy adaptive control algorithm is proposed, so that the position of machine sliding table can be tracked in real time according to external conditions in order to achieve stable control. Firstly, taking the CNC combination machine tool of central China as an example, this paper introduces the whole structure and function of the machine tool, then establishes the control model and transfer function of the machine tool sliding table, and designs the improved fuzzy adaptive controller, and finally, combines the AMEsim-MATLAB software to build the fuzzy adaptive control model of the electro-hydraulic position servo system sliding table, The combined experimental simulation of the built model is carried out. The experimental results show that the fuzzy Adaptive algorithm of machine tool sliding table based on electro-hydraulic position servo system has good position tracking effect compared with the traditional PID algorithm, the position control response speed is fast, the error is small and the robustness is strong, and the feasibility of the improved fuzzy Adaptive control algorithm is verified.

引文

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