Abstract
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.
This research is supported by the project (61273307) of the National Nature Science Foundation of China and the Fundamental Research Funds for the Central Universities (DUT12RC(3)97).
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Wang, X., Wang, J. (2014). Neurodynamics-Based Model Predictive Control for Trajectory Tracking of Autonomous Underwater Vehicles. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_21
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DOI: https://doi.org/10.1007/978-3-319-12436-0_21
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