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Shock and Vibration
Volume 2018 (2018), Article ID 3762784, 19 pages
Research Article

Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network

1Mechanical Engineering Program, Niigata University, Niigata 950-2181, Japan
2Department of Mechanical Engineering Informatics, Meiji University, Kawasaki 214-8571, Japan
3Akita University, Akita 010-8502, Japan

Correspondence should be addressed to Kazuhiko Hiramoto

Received 21 August 2017; Revised 25 October 2017; Accepted 4 December 2017; Published 15 January 2018

Academic Editor: Oren Lavan

Copyright © 2018 Kazuhiko Hiramoto et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.