Adaptive backstepping and sliding mode control of fin stabilizer based on RBF neural network

被引：1
|
作者：
Zhang, Yuantao [1 ]
Shi, Weiren [1 ]
Yin, Lingling [1 ]
Qiu, Mingbai [2 ]
Zhao, Lin [2 ]
机构：
[1] Chongqing Univ, Coll Automat, Chongqing 400030, Peoples R China
[2] Chongqing Hua Elect Instrument Chief Plant, Chongqing 400021, Peoples R China
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 2 | 2009年
关键词：
Fin stabilizer; ocean wave model; backstepping; sliding mode; RBF; PREDICTIVE CONTROL; DESIGN;
D O I：
10.1109/ICICISYS.2009.5358062
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Considering the influence of uncertainty as unknown nonlinearity, parameters perturbation and random waves disturbance to the fin stabilizer system during ship sailing in heavy sea, the random wave model is built and a robust controller based on adaptive backstepping, sliding mode and RBF neural network is proposed Adaptive backstepping and sliding mode control is the main controller and RBF neural network is used to compute the upper bound value of uncertainty which is composed of unknown nonlinearity, parameters perturbation and random waves disturbance, then the system stability is analyzed by using the Lyapunov theory The simulation results show that the control strategy is effective to decrease roll motion of fin stabilizer system in different sea conditions and has strong robust stability to overcome the uncertainty
引用
收藏
页码：302 / +
页数：2
相关论文
共 50 条
  • [41] Research on Sliding Mode Control for Robotic Manipulator Based on RBF Neural Network
    Gao, Wei
    Shi, Jianbo
    Wang, Wenqiang
    Sun, Yue
    [J]. 2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2017, : 4934 - 4938
  • [42] New Sliding Mode Control Based on Tracking Differentiator and RBF Neural Network
    Qu, Chunyu
    Hu, Yongzhuang
    Guo, Ziqi
    Han, Fangxu
    Wang, Xiuping
    [J]. ELECTRONICS, 2022, 11 (19)
  • [43] Adaptive RBF neural network based on sliding mode controller for active power filter
    Zhang, Huiyue
    Liu, Yunbo
    [J]. International Journal of Power Electronics, 2020, 11 (04) : 460 - 481
  • [44] Adaptive Wavelet Neural Network Backstepping Sliding Mode Tracking Control for PMSM Drive System
    Liu, Da
    Li, Muguo
    [J]. AUTOMATIKA, 2014, 55 (04) : 405 - 415
  • [45] A Novel Adaptive Sliding Mode Control of Robot Manipulator Based on RBF Neural Network and Exponential Convergence Observer
    Li, Xiaoling
    Gao, Hongliang
    Xiong, Lang
    Zhang, Hongcong
    Li, Bing
    [J]. NEURAL PROCESSING LETTERS, 2023, 55 (07) : 10037 - 10052
  • [46] A Novel Adaptive Sliding Mode Control of Robot Manipulator Based on RBF Neural Network and Exponential Convergence Observer
    Xiaoling Li
    Hongliang Gao
    Lang Xiong
    Hongcong Zhang
    Bing Li
    [J]. Neural Processing Letters, 2023, 55 : 10037 - 10052
  • [47] Sliding Mode Control Based on RBF Neural Networks
    Zhou Ya
    Wang Wu
    Jiao Xiao-bo
    [J]. FIFTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2012): COMPUTER VISION, IMAGE ANALYSIS AND PROCESSING, 2013, 8783
  • [48] Adaptive sliding mode control of maglev system based on RBF neural network minimum parameter learning method
    Sun, Yougang
    Xu, Junqi
    Qiang, Haiyan
    Chen, Chen
    Lin, GuoBin
    [J]. MEASUREMENT, 2019, 141 : 217 - 226
  • [49] Based on RBF Neural Network Adaptive Sliding Mode Shore Power Grid-connected Control Strategy
    Zhang, Cheng
    Gao, Jian
    Su, Zhen
    [J]. 2019 CHINESE AUTOMATION CONGRESS (CAC2019), 2019, : 1731 - 1735
  • [50] Adaptive sliding mode control of switched linear systems using disturbance observer based on the RBF neural network
    Hosseini, Jaber
    Rahmani, Zahra
    Noei, Abolfazl Ranjbar
    [J]. JOURNAL OF VIBRATION AND CONTROL, 2023, 29 (17-18) : 3952 - 3969
12345