Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network
We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency propertie...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/3762784 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558153067134976 |
|---|---|
| author | Kazuhiko Hiramoto Taichi Matsuoka Katsuaki Sunakoda |
| author_facet | Kazuhiko Hiramoto Taichi Matsuoka Katsuaki Sunakoda |
| author_sort | Kazuhiko Hiramoto |
| collection | DOAJ |
| description | We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN). Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA). The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances. |
| format | Article |
| id | doaj-art-152e1a9e0ba24304b5b2365b2ef1fa89 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-152e1a9e0ba24304b5b2365b2ef1fa892025-02-03T01:33:04ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/37627843762784Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural NetworkKazuhiko Hiramoto0Taichi Matsuoka1Katsuaki Sunakoda2Mechanical Engineering Program, Niigata University, Niigata 950-2181, JapanDepartment of Mechanical Engineering Informatics, Meiji University, Kawasaki 214-8571, JapanAkita University, Akita 010-8502, JapanWe propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN). Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA). The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances.http://dx.doi.org/10.1155/2018/3762784 |
| spellingShingle | Kazuhiko Hiramoto Taichi Matsuoka Katsuaki Sunakoda Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network Shock and Vibration |
| title | Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network |
| title_full | Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network |
| title_fullStr | Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network |
| title_full_unstemmed | Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network |
| title_short | Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network |
| title_sort | adaptive gain scheduled semiactive vibration control using a neural network |
| url | http://dx.doi.org/10.1155/2018/3762784 |
| work_keys_str_mv | AT kazuhikohiramoto adaptivegainscheduledsemiactivevibrationcontrolusinganeuralnetwork AT taichimatsuoka adaptivegainscheduledsemiactivevibrationcontrolusinganeuralnetwork AT katsuakisunakoda adaptivegainscheduledsemiactivevibrationcontrolusinganeuralnetwork |