Robust tracking control of an underwater vehicle and manipulator system based on double closed-loop integral sliding mode

A nonlinear robust control method for the trajectory tracking of the underwater vehicle and manipulator system that operates in the presence of external current disturbances is proposed using double closed-loop integral sliding mode control. The designed controller uses a double closed-loop control structure to track the desired trajectory in the joint space of the underwater vehicle and manipulator system, and its inner and outer loop systems use integral sliding surface to enhance the robustness of the whole system. Then, the continuous switching mode based on hyperbolic tangent function is used instead of the traditional discontinuous switching mode to reduce the chattering of the control input of the underwater vehicle and manipulator system. In addition, the control method proposed in this article does not need to estimate the uncertainties of the underwater vehicle and manipulator system control system through online identification, but also can ensure the robustness of the underwater vehicle and manipulator system motion control in underwater environment. Therefore, it is easier to be implemented on the embedded platform of the underwater vehicle and manipulator system and applied to the actual marine operation tasks. At last, the stability of the control system is proved by the Lyapunov theory, and its effectiveness and feasibility are verified by the simulation experiments in MATLAB software.

[1]  Giacomo Marani,et al.  Autonomous manipulation for an intervention AUV 217 , 2006 .

[2]  Donghee Kim,et al.  Trajectory generation and sliding-mode controller design of an underwater vehicle-manipulator system with redundancy , 2015 .

[3]  Zhiyu Zhou,et al.  Robust Kalman filtering with long short-term memory for image-based visual servo control , 2019, Multimedia Tools and Applications.

[4]  Laxman M. Waghmare,et al.  Disturbance estimator based non-singular fast fuzzy terminal sliding mode control of an autonomous underwater vehicle , 2018, Ocean Engineering.

[5]  Simon X. Yang,et al.  Adaptive terminal sliding mode Based Sensorless Speed control for underwater thruster , 2016, Int. J. Robotics Autom..

[6]  Sen Wang,et al.  Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning , 2018, Robotics Auton. Syst..

[7]  Yaming Wang,et al.  Sliding mode control based on a hybrid grey-wolf-optimized extreme learning machine for robot manipulators , 2019, Optik.

[8]  An Liu,et al.  Diving Adaptive Position Tracking Control for Underwater Vehicles , 2019, IEEE Access.

[9]  Bin Xu,et al.  Neuro-fuzzy control of underwater vehicle-manipulator systems , 2012, J. Frankl. Inst..

[10]  Martin Dekan,et al.  Moving obstacles detection based on laser range finder measurements , 2018 .

[11]  Hong Liu,et al.  A compact representation of human actions by sliding coordinate coding , 2017 .

[12]  Bin Xu,et al.  A sliding mode fuzzy controller for underwater vehicle-manipulator systems , 2005, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society.

[13]  Tao Zhang,et al.  Stable Adaptive Neural Network Control , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[14]  Junku Yuh,et al.  Self-adaptive neuro-fuzzy systems for autonomous underwater vehicle control , 2001, Adv. Robotics.

[15]  Peter King,et al.  Teach‐and‐repeat path following for an autonomous underwater vehicle , 2018, J. Field Robotics.

[16]  Ying Zhou,et al.  Heading tracking control with an adaptive hybrid control for under actuated underwater glider. , 2018, ISA transactions.

[17]  Kamal Youcef-Toumi,et al.  Control for Dynamic Positioning and Way-point Tracking of Underactuated Autonomous Underwater Vehicles Using Sliding Mode Control , 2019, J. Intell. Robotic Syst..

[18]  Sami El-Ferik,et al.  Adaptive containment control of multi-leader fleet of underwater vehicle-manipulator autonomous systems carrying a load , 2019, Int. J. Syst. Sci..

[19]  Jiang Wu,et al.  Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm , 2018 .

[20]  Yang Li Disturbance observer-based robust trajectory tracking control for a quadrotor UAV , 2015 .

[21]  Fei Wang,et al.  Backstepping Based Adaptive Region Tracking Fault Tolerant Control for Autonomous Underwater Vehicles , 2016, Journal of Navigation.

[22]  Mohammad Danesh,et al.  Design Boundary Layer Thickness and Switching Gain in SMC Algorithm for AUV Motion Control , 2019, Robotica.

[23]  Xingru Qu,et al.  Three-dimensional trajectory tracking control of an underactuated autonomous underwater vehicle based on ocean current observer , 2018, International Journal of Advanced Robotic Systems.

[24]  Sahjendra N. Singh,et al.  Nonlinear adaptive trajectory control of multi-input multi-output submarines with input constraints , 2016, J. Syst. Control. Eng..

[25]  Jingsong Xia,et al.  Multiple instance learning tracking based on Fisher linear discriminant with incorporated priors , 2018 .

[26]  Yi Han,et al.  Composite learning adaptive sliding mode control for AUV target tracking , 2019, Neurocomputing.

[27]  Cong Wang,et al.  Command filter based adaptive neural trajectory tracking control of an underactuated underwater vehicle in three-dimensional space , 2019, Ocean Engineering.

[28]  Nicolas Mansard,et al.  Task Sequencing for High-Level Sensor-Based Control , 2007, IEEE Transactions on Robotics.

[29]  J. Geoffrey Chase,et al.  Human-Robot Collaboration: A Literature Review and Augmented Reality Approach in Design , 2008 .

[30]  Xingru Qu,et al.  Three-dimensional path following control of underactuated autonomous underwater vehicle based on damping backstepping , 2017 .

[31]  Nilanjan Sarkar,et al.  Coordinated motion planning and control of autonomous underwater vehicle-manipulator systems subject to drag optimization , 2001 .

[32]  Mohammad Danesh,et al.  A Time Delay Controller included terminal sliding mode and fuzzy gain tuning for Underwater Vehicle-Manipulator Systems , 2015 .

[33]  Shuanghe Yu,et al.  Design of an indirect adaptive controller for the trajectory tracking of UVMS , 2018 .

[34]  Omid Elhaki,et al.  Neural network-based target tracking control of underactuated autonomous underwater vehicles with a prescribed performance , 2018, Ocean Engineering.

[35]  Jiang Wu,et al.  Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field , 2018 .

[36]  Junku Yuh,et al.  Dynamic analysis and two-time scale control for underwater vehicle-manipulator systems , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[37]  Laxman M. Waghmare,et al.  Uncertainty and disturbance estimator based sliding mode control of an autonomous underwater vehicle , 2017 .