Modeling and fuzzy control of ER damper using higher order spectra

Jeongmok Cho; Taegeun Jung; Dong-Hyeon Kim; Nam Huh; Tae-Whee Joung; Sujin Kim; Joongseon Joh

doi:10.1117/12.658391

27 March 2006 Modeling and fuzzy control of ER damper using higher order spectra

Jeongmok Cho, Taegeun Jung, Dong-Hyeon Kim, Nam Huh, Tae-Whee Joung, Sujin Kim, Joongseon Joh

Proceedings Volume 6166, Smart Structures and Materials 2006: Modeling, Signal Processing, and Control; 616615 (2006) https://doi.org/10.1117/12.658391
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2006, San Diego, California, United States

Abstract

Due to the inherent nonlinear nature of Electro-rheological(ER) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the nonlinear damping force model is made to identify the properties of the ER damper using higher order spectrum. The higher order spectral analysis is used to investigate the nonlinear frequency coupling phenomena with the damping force signal according to the sinusoidal excitation of the damper. Also, this paper presents an inverse model of the ER damper, i.e., the model can predict the required voltage so that the ER damper can produce the desired force for the requirement of vibration control of vehicle suspension systems. The inverse model has been constructed by using a multi-layer perceptron. A quarter-car suspension model is considered in this paper for analysis and simulation. Simulation results show that the proposed inverse model of ER damper can obtain control voltage of ER damper for required damping force.

Citation Download Citation

Jeongmok Cho, Taegeun Jung, Dong-Hyeon Kim, Nam Huh, Tae-Whee Joung, Sujin Kim, and Joongseon Joh "Modeling and fuzzy control of ER damper using higher order spectra", Proc. SPIE 6166, Smart Structures and Materials 2006: Modeling, Signal Processing, and Control, 616615 (27 March 2006); https://doi.org/10.1117/12.658391

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