A neurodynamics-based dynamic surface control algorithm for tracking control of dynamic positioning vehicles

In this paper, a neurodynamics-based dynamic surface control (DSC) algorithm is proposed, which is applied in tracking control of dynamic positioning (DP) vehicles. To avoid velocity jumps in the traditional DSC, we use the smooth continuous and bounded output characteristics of neural dynamic model to construct an intermediate virtue variable. And an appropriate Lyapunov function is introduced to get the tracking control law, which can guarantee global asymptotical stability of the control effect. Not only will this solve the problem of velocity jumps, but also can avoid generating excessive force and moment and meet the thrust constraints of DP vehicles propeller. The simulation results prove the superiority and effectiveness of the proposed algorithm.

[1]  Zhu Daqi,et al.  Plane trajectory tracking control of autonomous underwater vehicles based on a biologically inspired shunting model , 2011 .

[2]  Shi Xiao-cheng,et al.  Adaptive sliding-mode passive observer design for dynamic positioning vessel , 2013 .

[3]  Max Q.-H. Meng,et al.  A Bioinspired Neurodynamics-Based Approach to Tracking Control of Mobile Robots , 2012, IEEE Transactions on Industrial Electronics.

[4]  J. C. Gerdes,et al.  Dynamic surface control of nonlinear systems , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[5]  Asgeir J. Sørensen,et al.  A survey of dynamic positioning control systems , 2011, Annu. Rev. Control..

[6]  Xianyi Yang.  Neural network approaches to real-time motion planning and control of robotic systems , 1999 .

[7]  Yang Rui-rui.  Backstepping tracking control of autonomous underwater vehicles with bio-inspired neurdynamics model , 2012 .

[8]  Mingyu Fu,et al.  Adaptive dynamic surface tracking control for dynamic positioning cable laying vessel , 2012, ICAL.

[9]  J.B. de Sousa,et al.  Dynamic positioning concepts and strategies for the mobile offshore base , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[10]  Chaomin Luo,et al.  A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments , 2008, IEEE Transactions on Neural Networks.

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

[12]  Zhang Yi,et al.  Real-Time Robot Path Planning Based on a Modified Pulse-Coupled Neural Network Model , 2009, IEEE Transactions on Neural Networks.