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面向性能增强的双惯量伺服系统状态反馈控制

王树波 那靖 任雪梅

王树波, 那靖, 任雪梅. 面向性能增强的双惯量伺服系统状态反馈控制. 自动化学报, 2021, 47(x): 1−10 doi: 10.16383/j.aas.c200726
引用本文: 王树波, 那靖, 任雪梅. 面向性能增强的双惯量伺服系统状态反馈控制. 自动化学报, 2021, 47(x): 1−10 doi: 10.16383/j.aas.c200726
Wang Shu-Bo, Na Jing, Ren Xue-Mei. State feedback control for dual-inertia servo mechanisms with performance enhancement. Acta Automatica Sinica, 2021, 47(x): 1−10 doi: 10.16383/j.aas.c200726
Citation: Wang Shu-Bo, Na Jing, Ren Xue-Mei. State feedback control for dual-inertia servo mechanisms with performance enhancement. Acta Automatica Sinica, 2021, 47(x): 1−10 doi: 10.16383/j.aas.c200726

面向性能增强的双惯量伺服系统状态反馈控制

doi: 10.16383/j.aas.c200726
基金项目: 国家自然科学基金(61803216,, 61922037, 61873115,61973036)资助
详细信息
    作者简介:

    王树波:青岛大学自动化学院副教授, 主要研究方向为: 自适应控制, 智能控制, 自适应参数估计, 伺服系统控制及应用. E-mail: wangshubo1130@126.com

    那靖:昆明理工大学机电工程学院教授, 主要研究兴趣为: 智能控制, 自适应参数估计, 非线性控制及应用. 本文通信作者. E-mail: najing25@163.com

    任雪梅:北京理工大学自动化学院教授, 主要研究方向: 为非线性系统, 智能控制, 自适应控制及多电机驱动控制. E-mail: xmren@bit.edu.cn

State Feedback Control for Dual-Inertia Servo Mechanisms with Performance Enhancement

Funds: Supported by National Natural Science Foundation of China(61803216, 61922037, 61873115,61973036)
More Information
    Author Bio:

    WANG Shu-Bo Associate professor at the School of Automation, Qingdao University. His research interest covers adaptive control, intelligent control, adaptive parameter estimation, servo system control and applications

    NA Jing Professor at the Faulty of mechanical and Electrical Engineering Kunming University of Science and Technology. His current research interests include intelligent control, adaptive control, adaptive parameter estimation, neural network, nonlinear control and applications

    REN Xue-Mei Professor at the School of Automation, Beijing Institute of Technology. Her research interests include nonlinear systems, intelligent control, neural network control, adaptive control, and multi-drive servo systems

  • 摘要: 为避免使用函数逼近器(神经网络或模糊系统), 并提高双惯量伺服系统的瞬态响应和稳态性能, 本文针对含外部扰动的双惯量伺服系统, 提出一种基于预设性能函数的类比例状态反馈控制策略. 首先, 提出一种改进的带有最大超调、收敛速率以及稳态误差的预设性能函数, 并将该函数融入控制器设计使二惯量伺服的跟踪误差保持在预定的边界之内. 其次, 基于预设性能函数设计了类比例状态反馈控制器实现跟踪控制. 与传统基于函数逼近控制方法相比较, 该方法可降低控制系统计算复杂度同时消除反演控制中存在的复杂度爆炸问题. 最后, 利用双惯量伺服系统实验平台开展了对比实验, 验证了所提出的方法有效性和性能改进.
  • 图  1  双惯量伺服系统示意图

    Fig.  1  Schematic of the two-inertia elastic system

    图  2  改进的预设性能函数与原始预设性能函数比较: $\varphi_{0}=2$, $\varphi_{\infty}=0.2$以及(a) $a=1.5$; (b) $a=5$; (c) $a=10$.

    Fig.  2  Comparative profiles between the original PPF and the modified PPF with $\varphi_{0}=2$, $\varphi_{\infty}=0.2$ and (a) $a=1.5$; (b) $a=5$; (c) $a=10$.

    图  3  控制结构框图

    Fig.  3  Block diagram of the closed-loop system

    图  4  二惯量系统实验装置图

    Fig.  4  Schematic of the two-inertia elastic system

    图  5  正弦$x_d=3\sin(2\pi t/ 8)$跟踪性能:位置跟踪与跟踪误差

    Fig.  5  Tracking performance for$x_d=3\sin(2\pi t/ 8)$: Outputand tracking error

    图  6  正弦$x_d=8\sin(2\pi t/4)$跟踪性能:位置跟踪与跟踪误差

    Fig.  6  Tracking performance for$x_d=8\sin(2\pi t/4)$: Output and tracking error

    图  7  三种方法性能比较: 位置跟踪与跟踪误差

    Fig.  7  Performance comparison: Position tracking and tracking error

    图  8  阶跃信号: 位置跟踪与跟踪误差

    Fig.  8  Set-point: Position tracking and tracking error

    表  1  系统参数

    Table  1  System Parameters

    参数数值单位
    电机惯量${J_m}$0.026$ {\rm{kg \cdot {m^2}}}$
    负载惯量${J_l}$0.0113${\rm{kg \cdot {m^2}}}$
    弹性系数${K_l}$56${\rm{Nm/rad}}$
    下载: 导出CSV

    表  2  性能指标

    Table  2  Performance indexes

    方法$M_{e}$$\mu_{e}$$\sigma_{e}$
    本文方法0.15870.04130.00004
    动态面方法0.31040.11710.00009
    自适应神经控制0.30810.07570.00007
    下载: 导出CSV
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  • 收稿日期:  2020-09-07
  • 录用日期:  2021-03-02
  • 网络出版日期:  2021-03-27

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