Abstract：The trajectory tracking control problem of operational AUV (autonomous underwater vehicle) is addressed. In general, the stretching and operation processes of underwater manipulator will lead to changes of AUV dynamic performance, which will affect the AUV trajectory tracking control, and so does the water current. Aiming at the trajectory tracking control problem of AUV, an adaptive terminal sliding mode control method based on RBF (radial basis function) neural network is proposed. Under the framework of Lyapunov stability theory, the RBF neural network is used to approximate the changes of AUV dynamic performance caused by the stretching of the manipulator and the disturbance of the water current online. Then combined with the adaptive terminal sliding mode controller, the weights of neural network and control parameters of AUV are adaptively adjusted online. According to the Lyapunov stability theory, it is proved that the system trajectory tracking error of AUV is uniformly stable and bounded. Aiming at the chattering problem caused by the sliding mode control items, a chattering reduction method for the saturated continuous function with variable sliding mode gain is proposed to reduce the chattering of sliding mode control variables. Experiments on heading and vertical trajectory tracking are conducted to verify the effectiveness of the AUV system control method and the sliding mode chattering reduction method.
 徐玉如,李彭超.水下机器人发展趋势[J].自然杂志,2011,33(3):125-132.Xu Y R, Li P C. Developing tendency of unmanned underwater vehicles[J]. Chinese Journal of Nature, 2011, 33(3):125-132.
 Marani G, Choi S K, Yuh J. Underwater autonomous manipulation for intervention missions AUVs[J]. Ocean Engineering, 2009, 36(1):15-23.
 Londhe P S, Santhakumar M, Patre B M, et al. Task space control of an autonomous underwater vehicle manipulator system by robust single-input fuzzy logic control scheme[J]. IEEE Journal of Oceanic Engineering, 2017, 42(1):13-28.
 Mohan S, Kim J. Coordinated motion control in task space of an autonomous underwater vehicle-manipulator system[J]. Ocean Engineering, 2015, 104:155-167.
 张奇峰.自治水下机器人-机械手系统运动规划与协调控制研究[D].沈阳:中国科学院沈阳自动化研究所,2007.Zhang Q F. Research on coordinated motion planning and control of autonomous underwater vehicle-manipulator system[D]. Shenyang:Chinese Academy of Sciences, 2007.
 Bessa W M, Dutra M S, Kreuzer E. Depth control of remotely operated underwater vehicles using an adaptive fuzzy sliding mode controller[J]. Robotics and Autonomous Systems, 2008, 56(8):670-677.
 俞建成,张艾群,王晓辉,等.基于模糊神经网络水下机器人直接自适应控制[J].自动化学报,2007,33(8):840-846.Yu J C, Zhang A Q, Wang X H, et al. Direct adaptive control of underwater vehicles based on fuzzy neural networks[J]. Acta Automatica Sinica, 2007, 33(8):840-846.
 Bagheri A, Karimi T, Amanifard N. Tracking performance control of a cable communicated underwater vehicle using adaptive neural network controllers[J]. Applied Soft Computing, 2010, 10(3):908-918.
 Ho H F, Wong Y K, Rad A B. Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems[J]. Simulation Modelling Practice and Theory, 2009, 17(7):1199-1210.
 于靖,陈谋,姜长生.基于干扰观测器的非线性不确定系统自适应滑模控制[J].控制理论与应用,2014,31(8):993-999.Yu J, Chen M, Jiang C S. Adaptive sliding mode control for nonlinear uncertain systems based on disturbance observer[J]. Control Theory & Applications, 2014, 31(8):993-999.
 Wang Y J, Zhang M J, Wilson P A, et al. Adaptive neural network-based backstepping fault tolerant control for underwater vehicles with thruster fault[J]. Ocean Engineering, 2015, 110:15-24.
 冯子龙,刘健,刘开周.AUV自主导航航位推算算法的研究[J].机器人,2005,27(2):168-172.Feng Z L, Liu J, Liu K Z. Dead reckoning method for autonomous navigation of autonomous underwater vehicles[J]. Robot, 2005, 27(2):168-172.
 Avila J P J, Donha D C, Adamowski J C. Experimental model identification of open-frame underwater vehicles[J]. Ocean Engineering, 2013, 60:81-94.
 Zhang M J, Chu Z Z. Adaptive sliding mode control based on local recurrent neural networks for underwater robot[J]. Ocean Engineering, 2012, 45:56-62.
 Zhang M J, Liu X, Yin B J, et al. Adaptive terminal sliding mode based thruster fault tolerant control for underwater vehicle in time-varying ocean currents[J]. Journal of the Franklin Institute:Engineering and Applied Mathematics, 2015, 352(11):4935-4961.
 Mondal S, Mahanta C. Adaptive second order terminal sliding mode controller for robotic manipulators[J]. Journal of the Franklin Institute:Engineering and Applied Mathematics, 2014, 351(4):2356-2377.