Neurodynamics-based model predictive control of autonomous underwater vehicles in vertical plane

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 set-point control of the AUV. A one-layer recurrent neural network called the general projection neural network is applied for real-time optimization to compute optimal control vaiables. Simulation results are discussed to demonstrate the effectiveness and characteristics of the proposed model predictive control method.

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