Random Vibration of Hysteretic, Degrading Systems

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.

Section: Equations Of Motionmentioning

confidence: 99%

“…Equations ( 19) and ( 20) are approximated by the equivalent linear form using the equivalent linearization technique (Bouc, 1967;Baber and Wen, 1979) as…”

Section: Equations Of Motionmentioning

confidence: 99%

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

Prabakar

Sujatha

Narayanan

2014

“…In the present study, one parameter is fixed, ¼ 1 N=m. Thermodynamic examinations carried out by Baber and Wen (1981) showed that À must be satisfied. For the following calculations the two hysteresis parameters ¼ were set to be equal, which is equivalent to ¼ 0:5 in equation ( 5).…”

Section: Identification Proceduresmentioning

confidence: 99%

Low-frequency dynamics of an electrorheological fluid in squeeze and shear modes. Part II: modeling and identification

Bauer

Dohnal

Norrick

2013

Electrorheological fluids (ERFs) offer rapid control of damping using very low power requirements. Different models have been proposed to simulate the hysteresis phenomenon of ERFs. A modular test facility was designed to perform measurements of a specific electrorheological fluid in squeeze and shear modes in Part I. Based on these measurement cycles, material models for squeeze and shear modes are proposed and corresponding model parameters are identified within this parameter space. The fitted models are benchmarked against the measurement data and are capable of resembling the fluid's dynamic properties at harmonic excitation, including transitions between Bingham-like and viscoelastic material behavior. Once model parameters are identified, the dynamics of an ERF are resembled excellently by phenomenological models. However, clear trends within the parameter space cannot be stated for all model parameters which prevents the derivation of analytical statements for the model parameters. Since the identification of model parameters is not transferable, a new adaptation is necessary for every application. Nevertheless, the presented procedure can be applied directly in these cases and is robust.

“…The hysteretic displacements z 1f and z 1r are assumed to be governed by the modified Bouc-Wen model given by (Bouc, 1967;Baber and Wen, 1981;Prabakar et al, 2009).…”

Section: Half-car Modelmentioning

confidence: 99%

“…where C hf and C hr are the equivalent damping coefficients for the front and rear MR dampers, respectively and K hf and K hf are the equivalent stiffness for the front and rear MR dampers, obtained by minimizing the mean square equation error. y f and y r are the unsprung mass displacements (Baber and Wen, 1981).…”

Section: Equivalent Linearization Techniquementioning

confidence: 99%

Improving vehicle ride comfort using an active and semi-active controller in a half-car model

Marzbanrad

Poozesh

Damroodi

2012

In this paper, fuzzy logic control of a vehicle suspension with magneto rheological damper on a random road is considered. To simulate road roughness height a filtered white noise is employed and a four degree-of-freedom, half-car model is used in the analysis. A sensor mounted in the front of the vehicle is responsible for measuring the road irregularities at some distances in the front of the vehicle. Some other sensors are used to measure relative velocities of the vehicle body with respect to unsprung masses in wheel travel spaces. All measurements are assumed to be conducted in a noisy environment. The state variable of the simulated vehicle are calculated, using a method which is similar to the Kalman filter. To have a tailored estimation of magneto rheological damper performance a model which is known as Bouc-Wen is adopted. In addition, utilization of genetic algorithm (GA) in elicitation of fuzzy rules and tuning membership function is exhibited. This controller is a combination of fuzzy controller as a feed back and preview controller as a feed-forward controller, so as to anticipate irregularities of the road. Furthermore, GA is used to determine the optimized preview time. The typical parameters of fuzzy controller are encoded into a chromosome represented as an integer string. Then, by evaluating a multi objective function using a non dominated sorting GA II the superior chromosomes are elected and as a result, the optimized fuzzy logic controller can be employed. Also, by implementation of the combined controller which is designed by GA, it is possible to obtain better performances for the Fuzzy Interface System or, at the same performances, a less complex structure for the system. The effectiveness of the proposed algorithm has been verified through a series of simulations for a former designed controller.