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
1 刘厶源, 刘彦呈, 付俞鑫, 等. 基于动态模糊神经趋近律的水下航行器航迹跟踪控制[J]. 大连海事大学学报, 2016, 42 (4): 1- 6.
doi: 10.3969/j.issn.1671-7031.2016.04.001
LIU Siyuan , LIU Yancheng , FU Yuxin , et al. Trajectory tracking control of underwater vehicles based on dynamic fuzzy neural network reaching law[J]. Journal of Dalian Maritime University, 2016, 42 (4): 1- 6.
doi: 10.3969/j.issn.1671-7031.2016.04.001
2 DAS B , SUBUDHI B , PATI B B . Cooperative formation control of autonomous underwater vehicles: an overview[J]. International Journal of Automation and Computing, 2016, 13 (3): 199- 225.
doi: 10.1007/s11633-016-1004-4
3 CHEN B , LIU X P , TONG S C . Adaptive fuzzy output tracking control of MIMO nonlinear uncertain systems[J]. IEEE Transactions on Fuzzy Systems, 2007, 15 (2): 287- 300.
4 KAYACAN E , RAMON H , SAEYS W . Adaptive neuro-fuzzy control of a spherical rolling robot using sliding mode control theory based online learning algorithm[J]. IEEE Transactions on Cybernetics, 2013, 43 (1): 170- 179.
doi: 10.1109/TSMCB.2012.2202900
5 WANG W Y , CHIEN Y H , LEU Y G . Adaptive T-S fuzzy-neural modeling and control for general MIMO unknown nonaffine nonlinear systems using projection update laws[J]. Automatica, 2010, 46 (5): 852- 863.
doi: 10.1016/j.automatica.2010.02.024
6 HASSANEIN O , ANAVATTI S G , SHIM H . Model-based adaptive control system for autonomous underwater vehicles[J]. Ocean Engineering, 2016, 127, 58- 69.
doi: 10.1016/j.oceaneng.2016.09.034
7 CHANG Y H , CHAN W S , CHANG C W , et al. Adaptive fuzzy dynamic surface control for ball and beam system[J]. International Journal of Fuzzy Systems, 2011, 13 (1): 1- 7.
8 CHEN C S . Dynamic structure neural-fuzzy networks for robust adaptive control of robot manipulators[J]. IEEE Transactions on Industrial Electronics, 2008, 55 (9): 3402- 3414.
doi: 10.1109/TIE.2008.926778
9 ZHOU J , WEN C , ZHANG Y . Adaptive output control of non-linear systems with uncertain dead-zone nonlinearity[J]. IEEE Transactions on Automatic Control, 2006, 11 (2): 504- 511.
10 LIU Y J , TONG S C , WANG D . Adaptive neural output feedback controller design with reducedorder observer for a class of uncertain nonlinear SISO systems[J]. IEEE Transactions on Neural Networks and Learning Systems, 2011, 22 (8): 1328- 1334.
doi: 10.1109/TNN.2011.2159865
11 LEWIS F , TIM W K , WANG L Z . Dead-zone compensation in motion control systems using adaptive fuzzy logic control[J]. IEEE Transactions on Control System Technology, 1999, 7 (6): 731- 742.
doi: 10.1109/87.799674
12 LI Y , TONG S , LI T . Observer-based adaptive fuzzy tracking control of MIMO stochastic nonlinear systems with unknown control direction and unknown dead-zones[J]. IEEE Transactions on Fuzzy Systems, 2015, 23 (4): 1228- 1241.
13 LI Y , TONG S , LIU Y , LI T . Adaptive fuzzy robust output feedback control of nonlinear systems with unknown dead zones based on small-gain approach[J]. IEEE Transactions on Fuzzy Systems, 2014, 22 (1): 164- 176.
14 TONG S , LI Y . Adaptive fuzzy output feedback tracking backstepping control of strict-feedback nonlinear systems with unknown dead zones[J]. IEEE Transactions on Fuzzy Systems, 2012, 20 (1): 168- 180.
15 WANG N , QIAN C J , SUN J C , et al. Adaptive robust finite-time trajectory tracking control of fully actuated marine surface vehicles[J]. IEEE Transactions on Control System Technology, 2016, 24 (4): 1454- 1462.
16 WANG N , SUN J C , ER M J , et al. Adaptive robust online constructive fuzzy control of a complex surface vehicle system[J]. IEEE Transactions on Cybernetics, 2016, 46 (7): 1511- 1523.
doi: 10.1109/TCYB.2015.2451116
17 高剑. 无人水下航行器自适应非线性控制技术[M]. 西安: 西北工业大学出版社, 2016: 15- 32.
18 赵蕊, 余琨, 郑文成, 等. 无人水下航行器分布式运动控制系统设计与仿真验证[J]. 中国舰船研究, 2014, 9 (6): 92- 99.
doi: 10.3969/j.issn.1673-3185.2014.06.016
ZHAO Rui , YU Kun , ZHENG Wencheng , et al. Design and simulation of the distributive motion control system for unmanned underwater vehicles[J]. Chinese Journal of Ship Research, 2014, 9 (6): 92- 99.
doi: 10.3969/j.issn.1673-3185.2014.06.016
19 LIU Y C , LIU S Y , WANG N . Fully-tuned fuzzy neural network based robust adaptive tracking control of unmanned underwater vehicle with thruster dynamics[J]. Neuro Computing, 2016, 196, 1- 13.
20 FOSSEN T I . Marine control systems: guidance, navigation and control of ships rigs and underwater vehicles[M]. Trondheim, Norway: Marine Cybernetics AS, 2002: 1- 50.
21 JANG J O . A deadzone compensator of a DC motor system using fuzzy logic control[J]. IEEE Transactions on Systems Man and Cybernetics Applications and Reviews, 2001, 31 (1): 42- 48.
doi: 10.1109/5326.923267
22 GAO W , HUNG J C . Variable structure control of nonlinear systems: a new approach[J]. IEEE Transactions on Industrial Electronics, 1993, 40 (1): 45- 55.
23 ZHANG L J , QI X , PANG Y J . Adaptive output feedback control based on DRFNN for AUV[J]. Ocean Engineering, 2009, 36 (10): 716- 722.
24 CHEN B S , LEE C H . H∞tracking design of uncertain non-linear SISO systems: adaptive fuzzy approach[J]. IEEE Transactions on Fuzzy Systems, 1996, 4 (1): 32- 43.
25 WANG N , ER M J . Self-constructing adaptive robust fuzzy neural tracking control of surface vehicles with uncertainties and unknown disturbances[J]. IEEE Transactions on Control System Technology, 2015, 23 (3): 991- 1002.
26 GAO Y , ER M J . Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems[J]. IEEE Transactions on Fuzzy Systems, 2003, 11 (4): 462- 477.
27 WAI R J , MUTHUSAMY R . Fuzzy-neural-network inherited sliding-mode control for robot manipulator including actuator dynamics[J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24 (2): 274- 287.
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