Toggle Main Menu Toggle Search

Open Access padlockePrints

The Newcastle University research output collection, currently available on ePrints, will shortly be moving to a new open repository platform, Figshare. To prepare for the data migration we have paused adding new content to ePrints, and will resume once the new repository is launched. During this time you will continue to have access to ePrints (but no new content will appear). We will share updates here when available.

Block diagonal dominant remotely operated vehicle model simulation using decentralized model predictive control

Lookup NU author(s): Weipeng Lin, Professor Cheng ChinORCiD

Downloads


Licence

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Model predictive control on a highly-coupled open-frame remotely-operated vehicle (ROV) system subjected to uncertain disturbances has always been a challenge. A decentralized model predictive control (MPC) uses a design scaling to balance the interactions between the loops to achieve a block diagonal dominant ROV model is used. The numerical stability of the MPC algorithm improves despite the sensitivity of the control parameters on the output performance. The MPC gives a better 2-error norm performance and more tuning options to control the velocity and position output as compared to other design scaling methods and other controllers such as proportional-integral-derivative (PID), Fuzzy-logic, sliding-mode and PI-P cascaded control when subjected to underwater current disturbance.


Publication metadata

Author(s): Lin WP, Chin CS

Publication type: Article

Publication status: Published

Journal: Advances in Mechanical Engineering

Year: 2017

Volume: 9

Issue: 4

Pages: 1-24

Print publication date: 01/04/2017

Online publication date: 05/04/2017

Acceptance date: 16/02/2017

Date deposited: 18/04/2017

ISSN (print): 1687-8132

ISSN (electronic): 1687-8140

Publisher: Sage Publications Ltd.

URL: https://doi.org/10.1177/1687814017698886

DOI: 10.1177/1687814017698886


Altmetrics

Altmetrics provided by Altmetric


Share