An intelligent fuzzy control system with adapted interval for improving the supervisory performance in automation

被引:0
|
作者
Cheng-Li Liu
Shiaw-Tsyr Uang
Shun-Chi Kuo
机构
[1] Vanung University,Department of Industrial Management
[2] Overseas Chinese University,Department of Computer
来源
Operational Research | 2018年 / 18卷
关键词
Supervisory; Performance; Automated system; Fuzzy sets; Hit; False alarm;
D O I
暂无
中图分类号
学科分类号
摘要
Monitoring responsibilities include checking the automated operating system to make judgments and provide solutions. A loss of vigilance will lead to accidents if care is not taken. Therefore, emergency situations need to be quickly detected. The purpose of this study was to develop an intelligent fuzzy control system by using fuzzy sets to evaluate and improve the performance of supervisors. There are two input variables: fuzzy set S~\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tilde{S}$$\end{document}, which represents the linguistic notion “hit” when supervisor action is needed, and the fuzzy set Ñ, which represents the linguistic notion “false alarm” when no action is needed. The evaluation was extended from a two-value logic to a multi-value logic by using membership functions. The experimental results show that the fuzzy control used to evaluate the domain of decision response, i.e., the differences among a Type I error (miss), a Type II error (false alarm) and an appropriate reaction, was effective. Therefore, the traditional two-values logic was expanded to the multiple-values performance evaluation to clearly describe the difference in the judgment needed when monitoring “also this also other” work. In addition an alarm signal was produced by the fuzzy system for reminding participant’s attention. According to the results, the alarm was adapted to call the operator’s attention when a situation needed action to improve the supervisory performance. The results show that the effect of the fuzzy control alarm system for improving supervisory performance is significant. Additionally, the wide interval defined in fuzzy set would be more efficient to call participant’s attention and improve performance significantly than narrow.
引用
收藏
页码:689 / 709
页数:20
相关论文
共 50 条
  • [21] Direct Adaptive Fuzzy Control for Nonlinear Systems with Supervisory Control Performance
    Zheng, Ya-Qin
    Liu, Yan-Jun
    Tong, Shao-Cheng
    CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS, 2009, : 918 - 923
  • [22] Experimental Studies on Pressure Control Using Intelligent PI Fuzzy Supervisory Approach
    Sivashanmugam, Palani
    Kanagaraj, N.
    Kumar, R.
    INTERNATIONAL JOURNAL OF FOOD ENGINEERING, 2008, 4 (02)
  • [23] Hybrid intelligent system for supervisory control of mineral grinding process
    Ding, Jinliang
    Zhou, Ping
    Liu, Changxin
    Chai, Tianyou
    ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 1, 2006, : 979 - 984
  • [24] AN INTELLIGENT SUPERVISORY SYSTEM FOR ONLINE STATISTICAL PROCESS-CONTROL
    KAYA, A
    PROCEEDINGS OF THE 28TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-3, 1989, : 781 - 782
  • [25] Fuzzy Control System for Intelligent Car
    Wan, Xiao-feng
    Xing, Yi-si
    Cai, Li-xiang
    PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND NATURAL COMPUTING, VOL I, 2009, : 354 - 357
  • [26] Intelligent system for improving dosage control
    Rodrigues dos Santos, Fabio Cosme
    Henriques Librantz, Andre Felipe
    Dias, Cleber Gustavo
    Rodrigues, Sheila Gozzo
    ACTA SCIENTIARUM-TECHNOLOGY, 2017, 39 (01) : 33 - 38
  • [27] Robust output stabilization: Improving performance via supervisory control
    Efimov, D.
    Loria, A.
    Panteley, E.
    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2011, 21 (10) : 1219 - 1236
  • [28] Development of the automatic supervisory control system based on fuzzy inference
    Denisova, L. A.
    Alekseytsev, D. M.
    Meshcheryakov, V. A.
    MECHANICAL SCIENCE AND TECHNOLOGY UPDATE (MSTU 2019), 2019, 1260
  • [29] HGA fuzzy supervisory control system of a binary distillation column
    Pratishthananda, S
    Glankwamdee, W
    10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3: MEETING THE GRAND CHALLENGE: MACHINES THAT SERVE PEOPLE, 2001, : 501 - 504
  • [30] The automation control system of intelligent flexible clearing robot
    Fan, Jing
    INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2020, 17 (03)