Research on Anti-swaying of Crane Based on T-S Type Adaptive Neural Fuzzy Control

被引:0
|
作者
Wang, Zhao [1 ]
Shi, Yuhuan [1 ]
Li, Shurong [2 ]
机构
[1] China Univ Petr, Coll Informat & Control Engn, Qingdao 266580, Peoples R China
[2] Beijing Univ Posts & Telecommun, Automat Sch, Beijing 100876, Peoples R China
来源
PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC) | 2018年
关键词
Adaptive fuzzy neural network; T-S model; SNPRP conjugate gradient method; Anti-swing control;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Aiming at the swing problem of container cranes in the process of loading and unloading cargo, an adaptive neural fuzzy (ANFIS) control method based on Takagi-Sugeno (T-S) model is proposed in this paper. Firstly, the mathematical model of crane trolley-hoist system was established based on Lagrange's equation. Secondly, an improved T-S fuzzy neural network is proposed. Since the SNPRP conjugate gradient method has sufficient descent and global convergence under strong search conditions. In this paper. SNPRP conjugate gradient method is used to tram the premise parameters and the consequent parameters of T-S model. In order to obtain the best controller, the optimal control matrix of the system is obtained by linear quadratic optimal control using the minimum energy as an indicator, so that the neural network is used to train the ANFIS controller. Finally, the trained ANFIS controller is applied in the crane trolley-hoist system for simulation. The results show that this control method in this paper has better control effect and robustness under different rope lengths and different working conditions.
引用
收藏
页码:5503 / 5508
页数:6
相关论文
共 50 条
  • [11] Research on networked control system based on T-S model
    Lu Zhongda
    Zhang Fengbin
    Jia Yang
    Zhang Guoliang
    Ran Guangtao
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2019, 22 (Suppl 3): : S7459 - S7470
  • [12] Fuzzy Neural Network Based on Improved T-S Model and Its Application
    Huang, Zhiwei
    Zhou, Jianzhong
    Li, Chaoshun
    Li, Fengpan
    Zhang, Yongchuan
    ADVANCES IN NEURAL NETWORKS - ISNN 2009, PT 2, PROCEEDINGS, 2009, 5552 : 155 - 164
  • [13] Research on networked control system based on T-S model
    Lu Zhongda
    Zhang Fengbin
    Jia Yang
    Zhang Guoliang
    Ran Guangtao
    Cluster Computing, 2019, 22 : 7459 - 7470
  • [14] Study on Fuzzy-DMC Control System of T-S Fuzzy Model
    Wang, Shu-bin
    2017 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION SCIENCES (ICRAS), 2017, : 94 - 97
  • [15] The optimization of T-S fuzzy model feedback control matrix based on genetic arithmetic
    Guo, XJ
    Zhao, XL
    2002 IEEE REGION 10 CONFERENCE ON COMPUTERS, COMMUNICATIONS, CONTROL AND POWER ENGINEERING, VOLS I-III, PROCEEDINGS, 2002, : 1467 - 1470
  • [16] Automotive recall probability forecast based on T-S fuzzy neural network evaluation model
    Lian Lanxiang
    Yao Danya
    Huang Ling
    2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, : 3338 - 3343
  • [17] A T-S type of rough fuzzy controller based on process input-output data
    Huang, JJ
    Li, SY
    Man, CT
    42ND IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-6, PROCEEDINGS, 2003, : 4729 - 4734
  • [18] Stabilization of Interval Type-2 T-S Fuzzy Control Systems with Time Varying Delay
    Yang Feisheng
    Guan Shouping
    2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, : 3397 - 3401
  • [19] T-S fuzzy modeling and application based on satisfactory optimization
    Liu Jianfeng
    Gui Weihua
    Huang Zhiwu
    PROCEEDINGS OF THE 26TH CHINESE CONTROL CONFERENCE, VOL 4, 2007, : 446 - +
  • [20] Stability of T-S Model Based Fuzzy Control Systems -Virtual Equivalent System Approach
    Zhang, Weicun
    Wang, Xiaobo
    2013 INTERNATIONAL CONFERENCE ON COMPUTATIONAL PROBLEM-SOLVING (ICCP), 2013, : 90 - 93