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.