Model Predictive Control for Tracking of Underactuated Vessels Based on Recurrent Neural Networks

In this paper, a model predictive control (MPC) scheme is presented for tracking of underactuated vessels with only two available controls: namely, surge force and yaw moment. When no external disturbance is explicitly considered, the proposed MPC approach iteratively solves a formulated quadratic programming (QP) problem using a single-layer recurrent neural network called the general projection network over a finite receding horizon. When additive disturbances are taken into account, a reformulated minimax optimization problem is iteratively solved by using a two-layer recurrent neural network. The applied neural networks are both stable in the sense of Lyapunov and globally convergent to the exact optimal solutions of reformulated convex programming problems. Simulation results are provided to demonstrate the effectiveness and characteristics of the proposed neurodynamics-based MPC approaches to vessel tracking control.