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...

Full description

Saved in:
Bibliographic Details
Main Authors: Kazuhiko Hiramoto, Taichi Matsuoka, Katsuaki Sunakoda
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