An Adaptive Semiactive Control Algorithm for Magnetorheological Suspension Systems

[+] Author and Article Information
Xubin Song

Eaton Corp  Innovation Center, 26201 Northwestern Highway, Southfield, MI 48076xubinsong@eaton.com

Mehdi Ahmadian

Advanced Vehicle Dynamics Laboratory, Department of Mechanical Engineering,  Virginia Polytechnic Institute and State University, Blacksburg, VA 24060-0238

Steve Southward, Lane R. Miller

Thomas Lord Research Center,  Lord Corporation, 110 Lord Dr., Cary, NC 27511-7900

J. Vib. Acoust 127(5), 493-502 (Jan 28, 2005) (10 pages) doi:10.1115/1.2013295 History: Received July 03, 2003; Revised January 28, 2005

In this paper, we will present a nonlinear-model-based adaptive semiactive control algorithm developed for magnetorheological (MR) suspension systems exposed to broadband nonstationary random vibration sources that are assumed to be unknown or not measurable. If there exist unknown and∕or varying parameters of the dynamic system such as mass and stiffness, then the adaptive algorithm can include on-line system identification such as a recursive least-squares method. Based on a nonparametric MR damper model, the adaptive system stability is proved by converting the hysteresis inherent with MR dampers to a memoryless nonlinearity with sector conditions. The convergence of the adaptive system, however, is investigated through a linearization approach including further numerical illustration of specific cases. Finally the simulation results for a magnetorheological seat suspension system with the suggested adaptive control are presented. The results are compared with low-damping and high-damping cases, and such comparison further shows the effectiveness of the proposed nonlinear model-based adaptive control algorithm for damping tuning.

Copyright © 2005 by American Society of Mechanical Engineers
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Figure 1

Simple base-excited system representing a nonlinear MR seat suspension

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Figure 2

MR damping force time trace for different electrical currents

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Figure 3

Damping force-velocity characteristics for different electrical currents

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Figure 4

MR damper nonlinearity representation

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Figure 5

Illustration of sector condition for Φmr(∙) (i.e., Fs)

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Figure 6

Configuration of nonlinear feedback adaptive control

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Figure 7

(a) Unforced MR suspension system with time-variant hysteresis. (b) System with memoryless nonlinearity of Φmr∊[α,β]. (c) Hurwitz system with memoryless nonlinearity of Φ̂mr(∙)∊[0,β−α].

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Figure 8

Frequency spectrum of ISO excitation signals

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Figure 9

Acceleration RMS with respect to the tuning current

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Figure 10

Applied noise signals in frequency domain

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Figure 11

Applied noise signals in time domain

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Figure 12

Effect of system identification input filter on estimating the system mass and stiffness

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Figure 13

Effect of system identification input filter on adjusting the damper current

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Figure 14

Effect of system identification input filter on the seat acceleration

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Figure 15

Comparison of currents induced by adaptive control and passive damping

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Figure 16

Comparison of the seat acceleration induced by adaptive control and passive damping

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Figure 17

Damping comparison for ISO excitations

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Figure 18

Comparison of seat accelerations for ISO excitations with different control policies

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Figure 19

Online identification of mass and stiffness for adaptive control



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