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author:

Tian, Ye (Tian, Ye.) [1] | Li, Tieshan (Li, Tieshan.) [2] | Miao, Baobin (Miao, Baobin.) [3] | Luo, Weilin (Luo, Weilin.) [4] (Scholars:罗伟林)

Indexed by:

CPCI-S

Abstract:

In this paper, robust adaptive tracking control is proposed for the autonomous underwater vehicle (AUV) in the presence of external disturbance. Backstepping control of the system dynamics is introduced to develop full state feedback tracking control. Using backstepping control, minimal learning parameter (MLP) and variable structure control (VSC) based techniques, the robust adaptive tracking control is presented for AUV to handle the uncertainties and improve the robustness. The proposed controller guarantees that all the close-loop signals are semi-global uniform boundedness and that the tracking errors converge to a small neighborhood of the desired trajectory. Finally, simulation studies are given to illustrate the effectiveness of the proposed algorithm.

Keyword:

autonomous underwater vehicle minimal learning parameter neural network trajectory tracking

Community:

  • [ 1 ] [Tian, Ye]Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China
  • [ 2 ] [Li, Tieshan]Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China
  • [ 3 ] [Miao, Baobin]Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China
  • [ 4 ] [Luo, Weilin]Fuzhou Univ, Coll Mech Engn & Automat, Fuzhou 350108, Fujian, Peoples R China

Reprint 's Address:

  • [Li, Tieshan]Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China

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Source :

2016 EIGHTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI)

Year: 2016

Page: 372-377

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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