Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (3): 47-56.doi: 10.6040/j.issn.1672-3961.0.2018.385

• Machine Learning & Data Mining • Previous Articles     Next Articles

Robust adaptive self-organizing neuro-fuzzy tracking control of UUV with unknown dead-zone nonlinearity

Chuan MA1,2(),Yancheng LIU1,*(),Siyuan LIU1,Qinjin ZHANG1   

  1. 1. College of Marine Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China
    2. Department of Marine Engineering, Qingdao Ocean Shipping Mariners College, Qingdao 266071, Shandong, China
  • Received:2018-09-10 Online:2019-06-20 Published:2019-06-27
  • Contact: Yancheng LIU E-mail:machuan1984@126.com;liuyc@dlmu.edu.cn
  • Supported by:
    国家自然科学基金项目(51479018);中央高校基本科研业务费专项资金资助(3132016335)

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Abstract:

A robust adaptive self-organizing neuro-fuzzy control scheme for trajectory tracking of unmanned underwater vehicle with uncertainties and unknown dead-zone nonlinearity was proposed. The scheme adopted a novel sliding mode reaching law control framework and a self-organizing neuro-fuzzy network approximator to estimate the unknown dynamic and self-adaptive the parameter. The robust controller was employed to provide the finite L2-gain property to cope with reconstruction errors. Lyapunov stability theory analysis showed that tracking errors and their derivatives were stable and all signals in the closed-loop system were bounded. Comparative simulation results demonstrated the effectiveness and superiority of the proposed scheme, which could be a reference for the design of unmanned underwater vehicle.

Key words: UUV, robust adaptive tracking control, self-organizing neuro-fuzzy network, sliding mode reaching law control, unknown dead-zone nonlinearity, trajectory tracking

CLC Number: 

  • U665.2

Fig.1

Thruster allocation of UUV"

Fig.2

The structure of RASNFC scheme"

Fig.3

The comparisons of reference and actualtrajectories of UUV"

Fig.4

The comparisons of reference and actual attitudestates of UUV"

Fig.5

The derivatives comparison of reference and actualattitude states of UUV"

Fig.6

The tracking errors comparison for different control schemes"

Fig.7

The derivatives comparison of tracking errors for differentcontrol schemes"

Table 1

Performance comparison of different control schemes"

控制策略瞬态稳态性能运算时间/mse1e2e3e4e5${{\dot e}_1}$${{\dot e}_2}$${{\dot e}_3}$${{\dot e}_4}$${{\dot e}_5}$
AFSMC一般1.3546538216139651991712
FNNISMC一般1.1219429913124742671613
DSNFN较好1.0741334208067850491512
RASNFC很好0.7301050202020741051211
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