Model predictive control of autonomous underwater vehicles based on the simplified dual neural network

Zheng Yan, Siu Fong Chung, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Based on a recurrent neural network, a model predictive control (MPC) method for control of a class of autonomous underwater vehicles (AUVs) is presented. A coupled nonlinear kinematic model with constrains is considered. The model predictive control problem of AUVs is formulated as a time-varying quadratic programming problem, and a one-layer recurrent neural network called the simplified dual network is applied for real-time optimization. It is able to converge to the global optimal solution of the constrained optimization problem. Simulation results are discussed to demonstrate the effectiveness and characteristics of the proposed model predictive control method.

Original languageEnglish
Title of host publicationProceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Pages2551-2556
Number of pages6
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
Duration: 14 Oct 201217 Oct 2012

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Country/TerritoryKorea, Republic of
CitySeoul
Period14/10/1217/10/12

Keywords

  • Autonomous underwater vehicles
  • Model predictive control
  • Real-time optimization

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