Error-Driven-Based Nonlinear Feedback Recursive Design for Adaptive NN Trajectory Tracking Control of Surface Ships With Input Saturation

In this paper, we investigate the trajectory tracking control problem of surface ship subject to the dynamic uncertainties, unknown time-varying disturbances and input saturation. To handle the non-smooth input saturation nonlinearity and compensate the ship dynamic uncertainties, Gaussian error function and adaptive neural network technique are employed. In control design, to obtain the transient motion reference signal, finite-time nonlinear tracking differentiator is applied to generate virtual reference signal and to extract the derivative of virtual control law. Referring to the effects of the kinematics subsystem on the kinetics subsystem caused by the error of tracking differentiator, and the effects of the input saturation on the control accuracy and the dynamic quality of the trajectory tracking control system, we propose an error-driven-based nonlinear feedback recursive design technique to design trajectory tracking control law, and employ a new non-quadratic Lyapunov functions to analyze the trajectory tracking control system stability. The proposed control scheme fully embodies the characteristics of the lowgain and high-gain control, and overcomes the effect of tracking differentiator error on closed-loop system by recursive design method. Simulation results verify the effectiveness of our proposed control scheme.

[1]  Shuzhi Sam Ge,et al.  Boundary Control of a Coupled Nonlinear Flexible Marine Riser , 2010, IEEE Transactions on Control Systems Technology.

[2]  Shuzhi Sam Ge,et al.  Adaptive Neural Network Control of a Fully Actuated Marine Surface Vessel With Multiple Output Constraints , 2014, IEEE Transactions on Control Systems Technology.

[3]  Guang Wang,et al.  Event triggered trajectory tracking control approach for fully actuated surface vessel , 2016, Neurocomputing.

[4]  Yongduan Song,et al.  Robust adaptive tracking control of an underactuated ship with guaranteed transient performance , 2017, Int. J. Syst. Sci..

[5]  Alberto Broggi,et al.  Maritime Traffic Speed Enforcement , 2012, IEEE Intelligent Transportation Systems Magazine.

[6]  Wei Wang,et al.  Global stabilization control of underactuated ships with input saturation , 2015, 2015 34th Chinese Control Conference (CCC).

[7]  Rongrong Wang,et al.  Robust Composite Nonlinear Feedback Path-Following Control for Underactuated Surface Vessels With Desired-Heading Amendment , 2016, IEEE Transactions on Industrial Electronics.

[8]  Shuzhi Sam Ge,et al.  Vibration Control of Flexible Marine Riser Systems With Input Saturation , 2016, IEEE/ASME Transactions on Mechatronics.

[9]  Jun Wang,et al.  Distributed Containment Maneuvering of Multiple Marine Vessels via Neurodynamics-Based Output Feedback , 2017, IEEE Transactions on Industrial Electronics.

[10]  Woei Wan Tan,et al.  Tracking control of surface vessels via fault-tolerant adaptive backstepping interval type-2 fuzzy control , 2013 .

[11]  Guoqing Xia,et al.  Adaptive fuzzy control with backstepping for surface ships , 2013, 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD).

[12]  Shaocheng Tong,et al.  A DSC Approach to Robust Adaptive NN Tracking Control for Strict-Feedback Nonlinear Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Jing Sun,et al.  Path following of underactuated marine surface vessels using line-of-sight based model predictive control ☆ , 2010 .

[14]  Bao-Zhu Guo,et al.  Weak Convergence of Nonlinear High-Gain Tracking Differentiator , 2013, IEEE Transactions on Automatic Control.

[15]  Tieshan Li,et al.  Path following of underactuated surface vessels with fin roll reduction based on neural network and hierarchical sliding mode technique , 2015, Neural Computing and Applications.

[16]  Tieshan Li,et al.  Adaptive NN-DSC control design for path following of underactuated surface vessels with input saturation , 2017, Neurocomputing.

[17]  Arnau Doria-Cerezo,et al.  Passivity-based control applied to the dynamic positioning of ships , 2012 .

[18]  Kenneth R. Muske,et al.  Sliding-Mode Tracking Control of Surface Vessels , 2008, IEEE Transactions on Industrial Electronics.

[19]  Roger Skjetne,et al.  Modeling, identification, and adaptive maneuvering of CyberShip II: A complete design with experiments , 2004 .

[20]  Dongkyoung Chwa,et al.  Global Tracking Control of Underactuated Ships With Input and Velocity Constraints Using Dynamic Surface Control Method , 2011, IEEE Transactions on Control Systems Technology.

[21]  Yang Yang,et al.  Robust adaptive NN-based output feedback control for a dynamic positioning ship using DSC approach , 2014, Science China Information Sciences.

[22]  Asgeir J. Sørensen,et al.  Robust Dynamic Positioning of Offshore Vessels Using Mixed-μ Synthesis Modeling, Design, and Practice , 2017 .

[23]  Mou Chen,et al.  Actuator fault‐tolerant control of ocean surface vessels with input saturation , 2016 .

[24]  Feng Wei Yu,et al.  Robust Adaptive NN Design for Course-Tracking Control of Ship with Input Saturation , 2013 .

[25]  Darren M. Dawson,et al.  Adaptive output tracking control of a surface vessel , 2008, 2008 47th IEEE Conference on Decision and Control.

[26]  Zhu Qidan,et al.  Sliding mode tracking control of an underactuated surface vessel , 2012 .

[27]  Swaroop Darbha,et al.  Dynamic surface control for a class of nonlinear systems , 2000, IEEE Trans. Autom. Control..

[28]  Shuzhi Sam Ge,et al.  Adaptive NN Control of a Class of Nonlinear Systems With Asymmetric Saturation Actuators , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Roger Skjetne,et al.  Adaptive maneuvering, with experiments, for a model ship in a marine control laboratory , 2005, Autom..

[30]  Cong Wang,et al.  Identification and Learning Control of Ocean Surface Ship Using Neural Networks , 2012, IEEE Transactions on Industrial Informatics.

[31]  Wei He,et al.  Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function. , 2017, IEEE transactions on cybernetics.

[32]  Shuzhi Sam Ge,et al.  Adaptive dynamic positioning control for accommodation vessels with multiple constraints , 2017 .