Neurodynamics-Based Model Predictive Control for Trajectory Tracking of Autonomous Underwater Vehicles

This paper presents a model predictive control (MPC) method based on a recurrent neural network for control of autonomous underwater vehicles (AUVs) in a vertical plane. Both kinematic and dynamic models are considered in the trajectory tracking control of the AUV. A one-layer recurrent neural network called the simplified dual neural network is applied for real-time optimization to compute optimal control variables. Simulation results are discussed to demonstrate the effectiveness and characteristics of the proposed model predictive control method.

[1]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[2]  Jun Wang,et al.  A general projection neural network for solving optimization and related problems , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[3]  Jeen-Shing Wang,et al.  Self-adaptive recurrent neuro-fuzzy control of an autonomous underwater vehicle , 2002, IEEE Trans. Robotics Autom..

[4]  J. Chudley,et al.  An online genetic algorithm based model predictive control autopilot design with experimental verification , 2005 .

[5]  João P. Hespanha,et al.  Trajectory-Tracking and Path-Following of Underactuated Autonomous Vehicles With Parametric Modeling Uncertainty , 2007, IEEE Transactions on Automatic Control.

[6]  G. Antonelli.  On the Use of Adaptive/Integral Actions for Six-Degrees-of-Freedom Control of Autonomous Underwater Vehicles , 2007, IEEE Journal of Oceanic Engineering.

[7]  Przemyslaw Herman,et al.  Decoupled PD set-point controller for underwater vehicles , 2009 .

[8]  Jun Wang,et al.  Model predictive control for nonlinear affine systems based on the simplified dual neural network , 2009, 2009 IEEE Control Applications, (CCA) & Intelligent Control, (ISIC).

[9]  Jun Wang,et al.  A neurodynamic optimization approach to nonlinear model predictive control , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[10]  Jun Wang,et al.  Model predictive control of underwater gliders based on a one-layer recurrent neural network , 2013, 2013 Sixth International Conference on Advanced Computational Intelligence (ICACI).