Neurodynamics-based model predictive control for trajectory tracking of autonomous underwater vehicles

Xinzhe Wang, Jun Wang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)184-191
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8866
DOIs
Publication statusPublished - 2014
Externally publishedYes

Keywords

  • Autonomous underwater vehicles
  • Model predictive control
  • Simplified dual neural network

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