Neural Learning-Based Integrated Guidance and Control Algorithm of Multiple Underactuated AUVs

Ruiqi Mao, Rongxin Cui, Weisheng Yan, Lepeng Chen

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

4 Scopus citations

Abstract

This paper studies an enhanced intergrated guidance and control (IGC) algorithm of multiple underactuated autonomous underwater vehicles (AUVs). Considering the modelling errors and complex nonlinearities in the dynamics, an integrated mathematical model of guidance and control is established, and neural network (NN) approximation-based adaptive controller is used to deal with the uncertainties in the dynamics. A dynamic surface control (DSC) based recursive design procedure is adopted to calculate the actual control input. Effective tunning laws are proposed for each NN approximator which can guarantee a uniformly ultimately bounded (UUB) stability of the entired system with the use of rigorous Lyapunov-based stability proofs. Mathmatical simulation examples are carried out to corroborate the validity of the proposed algorithm.

Original languageEnglish
Title of host publication2018 IEEE 8th International Conference on Underwater System Technology
Subtitle of host publicationTheory and Application, USYS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538694053
DOIs
StatePublished - Dec 2018
Event8th IEEE International Conference on Underwater System Technology: Theory and Application, USYS 2018 - Wuhan, China
Duration: 1 Dec 20183 Dec 2018

Publication series

Name2018 IEEE 8th International Conference on Underwater System Technology: Theory and Application, USYS 2018

Conference

Conference8th IEEE International Conference on Underwater System Technology: Theory and Application, USYS 2018
Country/TerritoryChina
CityWuhan
Period1/12/183/12/18

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