Containment control of networked autonomous underwater vehicles guided by multiple leaders using predictor-based neural DSC approach

This paper considers the containment control of multiple autonomous underwater vehicles (AUVs) in the presence of model uncertainty and time-varying ocean disturbances. A new predictor-based neural dynamic surface control design approach is proposed to develop adaptive containment controllers, under which the trajectories of AUVs converge to the dynamic convex hull spanned by the dynamic leaders. The prediction errors are used to update the neural adaptive laws, which enables fast identifying the vehicle dynamics without excessive knowledge of their dynamical models. The stability properties of the closed-loop network are established via Lyapunov analysis, and the containment errors converge to an adjustable neighborhood of the origin. Comparative studies are given to show the effectiveness of the proposed method.

[1]  Tieshan Li,et al.  Leaderless and leader-follower cooperative control of multiple marine surface vehicles with unknown dynamics , 2013 .

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

[3]  Magnus Egerstedt,et al.  Distributed containment control with multiple stationary or dynamic leaders in fixed and switching directed networks , 2012, Autom..

[4]  Ziyang Meng,et al.  Distributed finite-time attitude containment control for multiple rigid bodies , 2010, Autom..

[5]  Demin Xu,et al.  Synchronization of multiple autonomous underwater vehicles without velocity measurements , 2012, Science China Information Sciences.

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

[7]  Mengyin Fu,et al.  Distributed containment control of multi‐agent systems with general linear dynamics in the presence of multiple leaders , 2013 .

[8]  Guodong Shi,et al.  Set tracking of multi-agent systems with variable topologies guided by moving multiple leaders , 2010, 49th IEEE Conference on Decision and Control (CDC).

[9]  Miroslav Krstic,et al.  Nonlinear and adaptive control de-sign , 1995 .

[10]  Shengyuan Xu,et al.  Distributed Containment Control with Multiple Dynamic Leaders for Double-Integrator Dynamics Using Only Position Measurements , 2012, IEEE Transactions on Automatic Control.

[11]  Guangfu Ma,et al.  Distributed containment control for Lagrangian networks with parametric uncertainties under a directed graph , 2012, Autom..

[12]  Ziyang Meng,et al.  Distributed Containment Control for Multiple Autonomous Vehicles With Double-Integrator Dynamics: Algorithms and Experiments , 2011, IEEE Transactions on Control Systems Technology.

[13]  Fernando Paganini,et al.  IEEE Transactions on Automatic Control , 2006 .

[14]  Dan Wang,et al.  Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form , 2005, IEEE Transactions on Neural Networks.

[15]  Peter Kuster,et al.  Nonlinear And Adaptive Control Design , 2016 .

[16]  Antonio M. Pascoal,et al.  Coordinated motion control of marine robots , 2003 .

[17]  Sung Jin Yoo,et al.  Distributed adaptive containment control of uncertain nonlinear multi-agent systems in strict-feedback form , 2013, Autom..

[18]  Leslie Hogben,et al.  Combinatorial Matrix Theory , 2013 .

[19]  Antonio M. Pascoal,et al.  Triangular formation control using range measurements: An application to marine robotic vehicles , 2012 .