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

被引：0
|
作者：
Wang, Xinzhe [1 ]
Wang, Jun [1 ]
机构：
[1] Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116023, Liaoning, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2014 | 2014年 / 8866卷
关键词：
Model predictive control; Autonomous underwater vehicles; Simplified dual neural network;
D O I：
10.1007/978-3-319-12436-0_21
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：184 / 191
页数：8
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