Time-Optimal Stabilization of Asynchronous Boolean Control Networks Under State and Control Constraints
This paper investigates the problem of time-optimal stabilization in Boolean control networks (BCNs) with an asynchronous update scheme, subject to state and control constraints. BCNs are fundamental models for various applications, including biological regulatory systems. While asynchronous updates...
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2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10933956/ |
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| author | Haonan Wang Shuping Wang Ruohai Xi Shuhua Gao |
| author_facet | Haonan Wang Shuping Wang Ruohai Xi Shuhua Gao |
| author_sort | Haonan Wang |
| collection | DOAJ |
| description | This paper investigates the problem of time-optimal stabilization in Boolean control networks (BCNs) with an asynchronous update scheme, subject to state and control constraints. BCNs are fundamental models for various applications, including biological regulatory systems. While asynchronous updates provide a more realistic representation, they introduce nondeterministic state transitions, posing significant challenges for control strategy design. We propose a systematic approach based on an iterative algorithm that efficiently computes the basin of attraction (BoA) of a steady state and constructs a state feedback control law to ensure time-optimal stabilization from any initial state in the BoA to the steady state. The algorithm explicitly accounts for state and control constraints. The correctness and time complexity of the proposed approach are rigorously analyzed. Notably, the time complexity remains relatively low since our algorithm only requires one-step state transition computations, avoiding the expensive matrix products commonly used in existing algebraic methods. To validate our approach, we apply it to an asynchronous BCN model of the myeloid differentiation process involving 11 genes. The results demonstrate the effectiveness and efficiency of our method in the theoretical analysis and control of asynchronous BCNs particularly modelling gene regulatory systems. Our algorithm code is publicly accessible at <uri>https://gitee.com/shuhuagao/tos-abcn</uri>. |
| format | Article |
| id | doaj-art-643137bd37f548ce85e3cb8bdb4d1fb1 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-643137bd37f548ce85e3cb8bdb4d1fb12025-08-20T03:03:49ZengIEEEIEEE Access2169-35362025-01-0113544505446110.1109/ACCESS.2025.355304010933956Time-Optimal Stabilization of Asynchronous Boolean Control Networks Under State and Control ConstraintsHaonan Wang0https://orcid.org/0009-0002-5371-8924Shuping Wang1Ruohai Xi2Shuhua Gao3https://orcid.org/0000-0003-3208-9620School of Electrical Engineering, Shandong University, Jinan, Shandong, ChinaCollaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of Shandong, Institute for Advanced Interdisciplinary Research (iAIR), University of Jinan, Jinan, Shandong, ChinaSchool of Control Science and Engineering, Shandong University, Jinan, Shandong, ChinaSchool of Control Science and Engineering, Shandong University, Jinan, Shandong, ChinaThis paper investigates the problem of time-optimal stabilization in Boolean control networks (BCNs) with an asynchronous update scheme, subject to state and control constraints. BCNs are fundamental models for various applications, including biological regulatory systems. While asynchronous updates provide a more realistic representation, they introduce nondeterministic state transitions, posing significant challenges for control strategy design. We propose a systematic approach based on an iterative algorithm that efficiently computes the basin of attraction (BoA) of a steady state and constructs a state feedback control law to ensure time-optimal stabilization from any initial state in the BoA to the steady state. The algorithm explicitly accounts for state and control constraints. The correctness and time complexity of the proposed approach are rigorously analyzed. Notably, the time complexity remains relatively low since our algorithm only requires one-step state transition computations, avoiding the expensive matrix products commonly used in existing algebraic methods. To validate our approach, we apply it to an asynchronous BCN model of the myeloid differentiation process involving 11 genes. The results demonstrate the effectiveness and efficiency of our method in the theoretical analysis and control of asynchronous BCNs particularly modelling gene regulatory systems. Our algorithm code is publicly accessible at <uri>https://gitee.com/shuhuagao/tos-abcn</uri>.https://ieeexplore.ieee.org/document/10933956/Boolean control networkasynchronous updatetime-optimal stabilizationstate constraintscontrol constraints |
| spellingShingle | Haonan Wang Shuping Wang Ruohai Xi Shuhua Gao Time-Optimal Stabilization of Asynchronous Boolean Control Networks Under State and Control Constraints IEEE Access Boolean control network asynchronous update time-optimal stabilization state constraints control constraints |
| title | Time-Optimal Stabilization of Asynchronous Boolean Control Networks Under State and Control Constraints |
| title_full | Time-Optimal Stabilization of Asynchronous Boolean Control Networks Under State and Control Constraints |
| title_fullStr | Time-Optimal Stabilization of Asynchronous Boolean Control Networks Under State and Control Constraints |
| title_full_unstemmed | Time-Optimal Stabilization of Asynchronous Boolean Control Networks Under State and Control Constraints |
| title_short | Time-Optimal Stabilization of Asynchronous Boolean Control Networks Under State and Control Constraints |
| title_sort | time optimal stabilization of asynchronous boolean control networks under state and control constraints |
| topic | Boolean control network asynchronous update time-optimal stabilization state constraints control constraints |
| url | https://ieeexplore.ieee.org/document/10933956/ |
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