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...

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Main Authors: Jiang, Jiang, Chang, Leilei, Zhang, Limao, Xu, Xiaojian
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160693
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Institution: Nanyang Technological University
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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
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