Adaptive control of unmanned surface vessels matching an optimized reference model

In this paper, adaptive control of unmanned marine surface vessel has been investigated in the presence of uncertain dynamics as well as external disturbance. Variable structure technique has been utilized in the control design to make the dynamic response and desired motion tracking as quickly as possible. In practice, usually the desired trajectories are given as step points, such that direct immediate tracking would cause large acceleration and huge control effort, while the latter may be out of the actuators' limitation. Therefore, a reference model approach is employed, such that instead of direct tracking of the desired set point, the control objective becomes to shape the closed-loop dynamics to match a reference model. The reference model is built using optimization technique which tries to minimizes both tracking error and motion acceleration, and subsequently control effort. The smooth tracking is thus guaranteed in an optimal manner.

[1]  B. Anderson,et al.  Optimal control: linear quadratic methods , 1990 .

[2]  Chenguang Yang,et al.  Neural Network-Based Motion Control of an Underactuated Wheeled Inverted Pendulum Model , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Marco Bibuli,et al.  Guidance of Unmanned Surface Vehicles: Experiments in Vehicle Following , 2012, IEEE Robotics & Automation Magazine.

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

[5]  Lino Marques,et al.  Robots for Environmental Monitoring: Significant Advancements and Applications , 2012, IEEE Robotics & Automation Magazine.

[6]  Roland Siegwart,et al.  Autonomous Inland Water Monitoring: Design and Application of a Surface Vessel , 2012, IEEE Robotics & Automation Magazine.

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

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

[9]  Thor I. Fossen,et al.  Guidance and control of ocean vehicles , 1994 .

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

[11]  Joel M. Esposito,et al.  Comprehensive framework for tracking control and thrust allocation for a highly overactuated autonomous surface vessel , 2011, J. Field Robotics.

[12]  Jing Li,et al.  Trajectory Planning and Optimized Adaptive Control for a Class of Wheeled Inverted Pendulum Vehicle Models , 2013, IEEE Transactions on Cybernetics.

[13]  Farbod Fahimi,et al.  Alternative trajectory-tracking control approach for marine surface vessels with experimental verification , 2013, Robotica.

[14]  Thor I. Fossen,et al.  Non-linear and adaptive backstepping designs for tracking control of ships , 1998 .

[15]  Thor I. Fossen,et al.  Nonlinear output feedback control of dynamically positioned ships using vectorial observer backstepping , 1998, IEEE Trans. Control. Syst. Technol..

[16]  N. Khaled,et al.  A dynamic model and a robust controller for a fully-actuated marine surface vessel , 2011 .

[17]  Cong Wang,et al.  Learning from adaptive neural network output feedback control of uncertain ocean surface ship dynamics , 2014 .

[18]  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.