Optimal Control Based on Reinforcement Learning for Flexible High-Rise Buildings with Time-Varying Actuator Failures and Asymmetric State Constraints
This study centers on the vibration suppression of high-rise building systems under extreme conditions, exploring a reinforcement learning (RL)-based vibration control strategy for flexible building systems with time-varying faults and asymmetric state constraints. A mathematical model precisely dep...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2075-5309/15/6/841 |
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| author | Min Li Rui Xie |
| author_facet | Min Li Rui Xie |
| author_sort | Min Li |
| collection | DOAJ |
| description | This study centers on the vibration suppression of high-rise building systems under extreme conditions, exploring a reinforcement learning (RL)-based vibration control strategy for flexible building systems with time-varying faults and asymmetric state constraints. A mathematical model precisely depicting the dynamic characteristics of flexible high-rise buildings, considering the time-varying nature of actuator faults, is initially established. Subsequently, a reinforcement learning-based controller is devised to counteract the negative impacts of faults on system performance. By introducing a time-varying asymmetric Lyapunov function, system state constraints are ensured, safeguarding system stability and security. The stability of the closed-loop system is rigorously proven using the Lyapunov stability theory, guaranteeing stable vibration suppression performance even in the presence of faults. The simulation results indicate that the proposed reinforcement learning vibration control method can effectively reduce the vibration response of flexible high-rise buildings when facing time-varying actuator faults. This demonstrates its remarkable robustness and adaptability, presenting a novel and effective solution for vibration control in real-world flexible high-rise buildings. |
| format | Article |
| id | doaj-art-dc55a2d78f9740eabb0e8f6c7e4dff00 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-dc55a2d78f9740eabb0e8f6c7e4dff002025-08-20T03:43:34ZengMDPI AGBuildings2075-53092025-03-0115684110.3390/buildings15060841Optimal Control Based on Reinforcement Learning for Flexible High-Rise Buildings with Time-Varying Actuator Failures and Asymmetric State ConstraintsMin Li0Rui Xie1School of Electrical and Control Engineering, North University of China, Taiyuan 030051, ChinaSchool of Electrical and Control Engineering, North University of China, Taiyuan 030051, ChinaThis study centers on the vibration suppression of high-rise building systems under extreme conditions, exploring a reinforcement learning (RL)-based vibration control strategy for flexible building systems with time-varying faults and asymmetric state constraints. A mathematical model precisely depicting the dynamic characteristics of flexible high-rise buildings, considering the time-varying nature of actuator faults, is initially established. Subsequently, a reinforcement learning-based controller is devised to counteract the negative impacts of faults on system performance. By introducing a time-varying asymmetric Lyapunov function, system state constraints are ensured, safeguarding system stability and security. The stability of the closed-loop system is rigorously proven using the Lyapunov stability theory, guaranteeing stable vibration suppression performance even in the presence of faults. The simulation results indicate that the proposed reinforcement learning vibration control method can effectively reduce the vibration response of flexible high-rise buildings when facing time-varying actuator faults. This demonstrates its remarkable robustness and adaptability, presenting a novel and effective solution for vibration control in real-world flexible high-rise buildings.https://www.mdpi.com/2075-5309/15/6/841flexible high-rise buildingsoptimal controltime-varying actuator faultsreinforcement learningasymmetric constraints |
| spellingShingle | Min Li Rui Xie Optimal Control Based on Reinforcement Learning for Flexible High-Rise Buildings with Time-Varying Actuator Failures and Asymmetric State Constraints Buildings flexible high-rise buildings optimal control time-varying actuator faults reinforcement learning asymmetric constraints |
| title | Optimal Control Based on Reinforcement Learning for Flexible High-Rise Buildings with Time-Varying Actuator Failures and Asymmetric State Constraints |
| title_full | Optimal Control Based on Reinforcement Learning for Flexible High-Rise Buildings with Time-Varying Actuator Failures and Asymmetric State Constraints |
| title_fullStr | Optimal Control Based on Reinforcement Learning for Flexible High-Rise Buildings with Time-Varying Actuator Failures and Asymmetric State Constraints |
| title_full_unstemmed | Optimal Control Based on Reinforcement Learning for Flexible High-Rise Buildings with Time-Varying Actuator Failures and Asymmetric State Constraints |
| title_short | Optimal Control Based on Reinforcement Learning for Flexible High-Rise Buildings with Time-Varying Actuator Failures and Asymmetric State Constraints |
| title_sort | optimal control based on reinforcement learning for flexible high rise buildings with time varying actuator failures and asymmetric state constraints |
| topic | flexible high-rise buildings optimal control time-varying actuator faults reinforcement learning asymmetric constraints |
| url | https://www.mdpi.com/2075-5309/15/6/841 |
| work_keys_str_mv | AT minli optimalcontrolbasedonreinforcementlearningforflexiblehighrisebuildingswithtimevaryingactuatorfailuresandasymmetricstateconstraints AT ruixie optimalcontrolbasedonreinforcementlearningforflexiblehighrisebuildingswithtimevaryingactuatorfailuresandasymmetricstateconstraints |