Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10247865" target="_blank" >RIV/61989100:27240/21:10247865 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27740/21:10247865

Result on the web

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9440916" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9440916</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2021.3083928" target="_blank" >10.1109/ACCESS.2021.3083928</a>

Result language

angličtina

Original language name

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

Original language description

Smart control systems are mostly applied in industry to control the movements of heavy machinery while optimizing overall operational efficiency. Major shipping companies use large quay cranes to load and unload containers from ships and still rely on the experience of on-site operators to perform transportation control procedures using joysticks and visual contact methods. This paper presents the research results of an EU-funded project for the Klaipeda container terminal to develop a novel container transportation security and cargo safety assurance method and system. It was concluded that many risks arise during the container handling procedures performed by the quay cranes and operators. To minimize these risks, the authors proposed controlling the sway of the spreader using a model predictive control method which applies a multi-layer perceptron (MLP) neural network (NN). The paper analyzes current neural network architectures and case studies and provides the engineering community with a unique case study which applies real operation statistical data. Several key training algorithms were tested, and the initial results suggest that the Levenberg-Marquardt (LM) algorithm and variable learning rate backpropagation perform better than methods which use the multi-layer perceptron neural network structure. CCBYNCND

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20204 - Robotics and automatic control

Project

<a href="/en/project/LM2018140" target="_blank" >LM2018140: e-Infrastructure CZ</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

—

Volume of the periodical

9

Issue of the periodical within the volume

May

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

78253-78265

UT code for WoS article

000739475100001

EID of the result in the Scopus database

2-s2.0-85107197007