Artificial Potential-Based Adaptive ${{H}_{\infty }}$ Synchronized Tracking Control for Accommodation Vessel

Combining with artificial potential field and robust <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math> </inline-formula> methods, the neural network (NN)-based adaptive synchronized tracking control is proposed for accommodation vessel (AV). The control task is to drive AV synchronous tracking floating production storage and offloading (FPSO). For finishing the task, NN is employed to approximate the unknown nonlinear dynamics of AV; <inline-formula><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> method is to guarantee the system states of AV robust to exogenous disturbances; artificial potential method aims to produce the attractive and repulsive forces to assist AV maintaining desired distance with FPSO so that the gangway connecting both AV and FPSO is operated smoothly. Finally, it is proven that the proposed control scheme can guarantee that all error signals of the tracking control are Semi-Globally Uniformly Ultimately Bounded (SGUUB) and AV can synchronously track FPSO to desired accuracy. The simulation results further demonstrate the effectiveness of the proposed method.

[1]  Tao Zhang,et al.  Adaptive neural network control of nonlinear systems by state and output feedback , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[2]  George J. Pappas,et al.  Stable flocking of mobile agents, part I: fixed topology , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[3]  Changjiu Zhou,et al.  Adaptive fuzzy H/sub /spl infin// stabilization for strict-feedback canonical nonlinear systems via backstepping and small-gain approach , 2005, IEEE Transactions on Fuzzy Systems.

[4]  Shuzhi Sam Ge,et al.  Robust Adaptive Position Mooring Control for Marine Vessels , 2013, IEEE Transactions on Control Systems Technology.

[5]  Yeong-Chan Chang,et al.  Adaptive fuzzy-based tracking control for nonlinear SISO systems via VSS and H∞ approaches , 2001, IEEE Trans. Fuzzy Syst..

[6]  Leigh McCue,et al.  Handbook of Marine Craft Hydrodynamics and Motion Control [Bookshelf] , 2016, IEEE Control Systems.

[7]  Thor I. Fossen,et al.  Marine Control Systems Guidance, Navigation, and Control of Ships, Rigs and Underwater Vehicles , 2002 .

[8]  Long Cheng,et al.  Decentralized Robust Adaptive Control for the Multiagent System Consensus Problem Using Neural Networks , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Zengqiang Chen,et al.  Formation control of multi-agent system based on potential function , 2008, 2008 Asia Simulation Conference - 7th International Conference on System Simulation and Scientific Computing.

[10]  Gaurav S. Sukhatme,et al.  Mobile Sensor Network Deployment using Potential Fields : A Distributed , Scalable Solution to the Area Coverage Problem , 2002 .

[11]  Keng Peng Tee,et al.  Control of fully actuated ocean surface vessels using a class of feedforward approximators , 2006, IEEE Transactions on Control Systems Technology.

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

[13]  Danilo Rastovic,et al.  Targeting and synchronization at tokamak with recurrent artificial neural networks , 2012, Neural Computing and Applications.

[14]  Bor-Sen Chen,et al.  Robust H∞ filtering for nonlinear stochastic systems , 2005 .

[15]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[16]  Ming-Chang Hwang,et al.  A robust position/force learning controller of manipulators via nonlinear H infin control and neural networks , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[17]  Danilo Rastovic,et al.  Fuzzy Scaling and Stability of Tokamaks , 2009 .

[18]  Yusong Cao,et al.  Dynamic positioning of drilling vessels with a fuzzy logic controller , 2002, Int. J. Syst. Sci..

[19]  Sergiu-Dan Stan,et al.  A Novel Robust Decentralized Adaptive Fuzzy Control for Swarm Formation of Multiagent Systems , 2012, IEEE Transactions on Industrial Electronics.

[20]  Daniel E. Koditschek,et al.  Exact robot navigation using artificial potential functions , 1992, IEEE Trans. Robotics Autom..

[21]  Weihai Zhang,et al.  State Feedback HINFINITY Control for a Class of Nonlinear Stochastic Systems , 2006, SIAM J. Control. Optim..

[22]  Duo Li,et al.  DYNAMIC POSITIONING OF SHIPS USING A PLANNED NEURAL NETWORK CONTROLLER , 1995 .

[23]  Shuzhi Sam Ge,et al.  Neural network tracking control of ocean surface vessels with input saturation , 2009, 2009 IEEE International Conference on Automation and Logistics.

[24]  W. Zhang,et al.  State Feedback H_∞Control for a Class of Nonlinear Stochastic Systems , 2009 .

[25]  Thor I. Fossen,et al.  Handbook of Marine Craft Hydrodynamics and Motion Control: Fossen/Handbook of Marine Craft Hydrodynamics and Motion Control , 2011 .

[26]  Tsu-Tian Lee,et al.  Hinfin tracking-based sliding mode control for uncertain nonlinear systems via an adaptive fuzzy-neural approach , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[27]  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.