JRM Vol.7 No.4 pp. 295-300
doi: 10.20965/jrm.1995.p0295


Integration of Bilinear Systems and Neural Networks for Designing Nonlinear Semi-Active Suspensions

Antonio Moran, Tomohiro Hasegawa and Masao Nagai

Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, 2-24-16, Nakamachi, Koganeishi, Tokyo, 184, Japan

June 19, 1995
June 30, 1995
August 20, 1995
Semi-active suspension, Neural networks, Nonlinear identification, Nonlinear optimal control, Ride quality, Vibration isolation

This paper presents a new design method of semi-active suspensions based on the integration of neural networks and bilinear systems. It is known that semi-active suspensions with ideal linear components have a bilinear structure. However actual semi-active suspensions with nonlinear components have an structure which is not purely bilinear. In order to improve the performance of semi-active suspensions, neural networks and bilinear systems are integrated and used for the identification and optimal control of nonlinear semi-active suspensions. The validity and applicability of the proposed method are analyzed and verified theoretically and experimentally using a semi-active suspension model equipped with piezoelectric actuators.

Cite this article as:
Antonio Moran, Tomohiro Hasegawa, and Masao Nagai, “Integration of Bilinear Systems and Neural Networks for Designing Nonlinear Semi-Active Suspensions,” J. Robot. Mechatron., Vol.7, No.4, pp. 295-300, 1995.
Data files:

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021