Retraceable and online multi-objective active optimal control using belief rule base
A new approach that employs the belief rule base (BRB) is proposed in this study with the goal of Retraceable and Online Multi-objective Active (ROMA) optimal control for complex systems, namely ROMA-BRB. Active optimal control means that multiple objectives are controlled by actively identifying an...
Saved in:
Main Authors: | , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/160693 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-160693 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1606932022-08-01T03:35:00Z Retraceable and online multi-objective active optimal control using belief rule base Jiang, Jiang Chang, Leilei Zhang, Limao Xu, Xiaojian School of Civil and Environmental Engineering Engineering::Civil engineering Active Optimal Control Retraceability A new approach that employs the belief rule base (BRB) is proposed in this study with the goal of Retraceable and Online Multi-objective Active (ROMA) optimal control for complex systems, namely ROMA-BRB. Active optimal control means that multiple objectives are controlled by actively identifying and optimizing key factors in the input. The retraceability requirement means that the procedural and final outputs can be traced back to the inputs to provide maximum accountability. The online requirement is met if the procedures are derivable and only adopting deterministic optimization approaches. To meet those requirements, BRB is adopted owing to its derivable procedures as a white box. There are four major steps in the new ROMA-BRB approach. First, multiple BRBs are constructed for multiple outputs. Then, the contribution of each factor in the input made to each output is calculated. Third, key factors are identified by comparing their contributions to multiple outputs. Finally, the identified key factors are optimized for actively controlling multi-objectives by pushing the Pareto frontier in an online manner. A practical tunnel-induced safety control case is studied whose goal is to reduce both the settlement and the building tilt rate (BTR). Case study results validate that both the settlement and BTR are effectively reduced by optimizing only the key operational factors. The validity of the proposed ROMA-BRB approach is also confirmed by comparing the results of pursuing only one objective, as well as using different numbers of key factors and other weight settings. Ministry of Education (MOE) Nanyang Technological University This work was supported by the Ministry of Education Tier 1 Grant, Singapore (No. 04MNP002126C120; No. 04MNP0002 79C120) and the Start-Up Grant at Nanyang Technological University, Singapore (No. 04INS000423C120). This work is also supported by the National Natural Science Foundation of China (Grant Nos. 71671186, 71601180, 61903018), and the Natural Science Foundation of Zhejiang Province, China (LY21F030011). 2022-08-01T03:35:00Z 2022-08-01T03:35:00Z 2021 Journal Article Jiang, J., Chang, L., Zhang, L. & Xu, X. (2021). Retraceable and online multi-objective active optimal control using belief rule base. Knowledge-Based Systems, 233, 107553-. https://dx.doi.org/10.1016/j.knosys.2021.107553 0950-7051 https://hdl.handle.net/10356/160693 10.1016/j.knosys.2021.107553 2-s2.0-85116705969 233 107553 en 04MNP002126C120 04MNP000279C120 04INS000423C120 Knowledge-Based Systems © 2021 Elsevier B.V. All rights reserved. |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Civil engineering Active Optimal Control Retraceability |
spellingShingle |
Engineering::Civil engineering Active Optimal Control Retraceability Jiang, Jiang Chang, Leilei Zhang, Limao Xu, Xiaojian Retraceable and online multi-objective active optimal control using belief rule base |
description |
A new approach that employs the belief rule base (BRB) is proposed in this study with the goal of Retraceable and Online Multi-objective Active (ROMA) optimal control for complex systems, namely ROMA-BRB. Active optimal control means that multiple objectives are controlled by actively identifying and optimizing key factors in the input. The retraceability requirement means that the procedural and final outputs can be traced back to the inputs to provide maximum accountability. The online requirement is met if the procedures are derivable and only adopting deterministic optimization approaches. To meet those requirements, BRB is adopted owing to its derivable procedures as a white box. There are four major steps in the new ROMA-BRB approach. First, multiple BRBs are constructed for multiple outputs. Then, the contribution of each factor in the input made to each output is calculated. Third, key factors are identified by comparing their contributions to multiple outputs. Finally, the identified key factors are optimized for actively controlling multi-objectives by pushing the Pareto frontier in an online manner. A practical tunnel-induced safety control case is studied whose goal is to reduce both the settlement and the building tilt rate (BTR). Case study results validate that both the settlement and BTR are effectively reduced by optimizing only the key operational factors. The validity of the proposed ROMA-BRB approach is also confirmed by comparing the results of pursuing only one objective, as well as using different numbers of key factors and other weight settings. |
author2 |
School of Civil and Environmental Engineering |
author_facet |
School of Civil and Environmental Engineering Jiang, Jiang Chang, Leilei Zhang, Limao Xu, Xiaojian |
format |
Article |
author |
Jiang, Jiang Chang, Leilei Zhang, Limao Xu, Xiaojian |
author_sort |
Jiang, Jiang |
title |
Retraceable and online multi-objective active optimal control using belief rule base |
title_short |
Retraceable and online multi-objective active optimal control using belief rule base |
title_full |
Retraceable and online multi-objective active optimal control using belief rule base |
title_fullStr |
Retraceable and online multi-objective active optimal control using belief rule base |
title_full_unstemmed |
Retraceable and online multi-objective active optimal control using belief rule base |
title_sort |
retraceable and online multi-objective active optimal control using belief rule base |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/160693 |
_version_ |
1743119532553142272 |