Stationary response of a quarter car vehicle model with magnetorheological damper

Prabakar, R.S.; Sujatha, C.; Narayanan, S.

doi:10.1504/ijvsmt.2008.025403

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…In this section, the MR damper model parameters for the front and rear suspension systems corresponding to the half-car model are obtained optimally by solving a MOOP using a combination of the NSGA II algorithm and the Pareto optimal scheme (Deb, 2002). NSGA II is explained in Prabakar et al (2008Prabakar et al ( , 2009Prabakar et al ( , 2013. The following subsections deal with the procedure for obtaining the optimal values of the MR damper parameters.…”

Section: Limited State Feedback Control Lawmentioning

confidence: 99%

“…The parameters of the MR damper based on different parametric models were determined experimentally. Prabakar et al (2008) developed a twin-tube MR damper for vehicular applications and characterized the damper behaviour using modified Bouc-Wen model parameters, which were obtained optimally using a multi-objective optimization technique. The stationary response in terms of the zerolag covariance of a two degrees of freedom (DOF) quarter-car vehicle model with nonlinear suspension consisting of linear elements and a nonlinear MR damper to random road excitation, can be obtained by the equivalent linearization method and verified by Monte Carlo simulation.…”

Section: Introductionmentioning

confidence: 99%

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Response of a half-car model with optimal magnetorheological damper parameters

Prabakar

Sujatha

Narayanan

2014

Journal of Vibration and Control

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In this paper, control of the stationary response of a half-car model with a magnetorheological (MR) damper moving over a random road is considered. The MR damper is characterized using Bingham and modified Bouc–Wen models whose parameters are determined optimally using a multi-objective optimization technique and nondominated sorting genetic algorithm II. The multi-objective optimization problem is solved by minimizing the difference between the root mean square sprung mass acceleration, the sum of the front and rear suspension strokes and the sum of the front and rear road holding response of the half-car model with the MR damper, and those of an active suspension system based on linear quadratic regulator (LQR) control. The control force of the MR damper suspension is constrained to lie within ±5% of the control force corresponding to the active suspension system based on LQR control. It is observed that the MR damper suspension systems with optimal parameters perform an order of magnitude better than the passive suspension and perform as well as active suspensions with limited state feedback, and closer to the performance of fully active suspensions. In the case of MR damper suspensions, the vehicle response statistics are obtained using the equivalent linearization method and verified by Monte Carlo simulation.

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Section: Limited State Feedback Control Lawmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Response of a half-car model with optimal magnetorheological damper parameters

Prabakar

Sujatha

Narayanan

2014

Journal of Vibration and Control

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Intelligent semi-active vehicle suspension systems using neural networks

Kanarachos

2012

IJVSMT

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A methodology is proposed for designing intelligent vehicle suspension systems which implement the magneto-rheological damper as an active element. The adaptive control law is constructed based on the neural network methodology and Taylor series approximation. The controller commands the current of the magneto-rheological damper and controls directly the damper force. The neural network is trained with respect to a developed road disturbance scenario and its parameters are obtained using a global numerical optimisation technique. The proposed control law has a novel structure capable of sensing the interactions between the variables and thus can adjust the feedback gains with respect to the existing conditions. It takes into consideration the actuator's dynamics and avoids limit cycling which is caused by the hysteretic behaviour of the MR dampers. The performance of the intelligent system is evaluated by means of simulations in MATLAB for quarter and half car models.

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Stationary response of a quarter car vehicle model with magnetorheological damper

Cited by 2 publications

References 15 publications

Response of a half-car model with optimal magnetorheological damper parameters

Response of a half-car model with optimal magnetorheological damper parameters

Intelligent semi-active vehicle suspension systems using neural networks

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