Development of Methodology for Semi Active Suspension System Using MR Damper

Yerrawar, R. N.; Arakerimath, R. R.

doi:10.1016/j.matpr.2017.07.289

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…[19][20][21] A Nonlinear Filter based observer structure has been proposed in, 22 which takes the nonlinearities of the damper into account. Magnetorheological dampers have been investigated through research work carried out from, [23][24][25][26] which is a feedback control for a half and full-vehicle suspension system. An active suspension for the quarter, half, and full vehicle model was developed in the studies conducted from.…”

Section: Introductionmentioning

confidence: 99%

Active and Passive Suspension System Performance under Random Road Profile Excitations

Kumar¹,

Medhavi²,

Kumar³

2020

The International Journal of Acoustics and Vibration

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Passive suspensions are designed to satisfy the conflicting criteria of riding comfort and vehicle handling. An active suspension system attempts to overcome these compromises to provide the best performance for vehicle control. Different types of mathematical models have been used to study the suspension system of a vehicle. The quarter vehicle model is used for initial investigation. Later, the half vehicle and full vehicle models are used for the study, which is closer to the actual model of a vehicle suspension. In this paper, the behavior of a suspension system is analyzed using the full vehicle model. In the current work, the dynamic equation and their state-space formulation are presented for the full vehicle model to understand the system prior to the controller design. The open-loop response of the full vehicle suspension system, when subjected to various road excitations, is also studied. The procedure of modeling a SIMULINK model for passive suspensions system is discussed in detail. Design of the simple Proportional Integral Derivative (PID) feedback and feed-forward controller is presented for the active suspension system using transfer functions. Closed-loop transfer functions are also derived and their responses are plotted. To analyze the rollover behavior simulation for cornering is also performed in the current study.

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Section: Introductionmentioning

confidence: 99%

Active and Passive Suspension System Performance under Random Road Profile Excitations

Kumar¹,

Medhavi²,

Kumar³

2020

The International Journal of Acoustics and Vibration

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show abstract

“…This has led to the emergence of new research areas aimed at optimizing and improving suspension systems [2,3] In general, suspension systems link the vehicle body and wheels, allowing for relative movement between them [4]. Such systems are classified into three categories in the literature: passive, semiactive, and active suspension systems [5,6,7]. While passive systems were utilized in earlier days, they have become inadequate in terms of comfort with the advent of semi-active and active systems [8].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Suspension System Control Performance Using PID Controller Incorporated Low-Pass Filter Optimizated with Genetic Algorithm

Şenaslan,

Bilgiç

2024

DEUFMD

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In this study, a filter has been incorporated to enhance the performance of the PID controller, which is commonly used for controlling suspension systems. While designing this filter, the insipration has been the low-pass filter used in sliding mode controllers to prevent chattering and uncertainties in system parameters, unlike conventional PID controller filters. Additionally, the filtered force value was combined with the force value obtained from the PID controller using an equation based on a coefficient, and filter coefficients were optimized through genetic algorithms. As a result of the optimization, the designed controller was simulated for various road inputs that could be encountered, and results were obtained. By comparing the results obtained with a PID controller without a filter and without a controller, the performance of the designed controller is clearly shown according to IAE and ISE criteria. Robustness of the controller was evaluated under varying mass conditions and its performance was given as a table.

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“…Thus, the control suspensions or semiactive suspensions were researched and applied on the vehicles to better improve the comfort quality. [4][5][6] In controlling process of semiactive suspensions, the PID control was applied for computing active forces in suspensions. 7,8 This control method was simple and depends only on determining the control parameters of K p , K i , and K d .…”

Section: Introductionmentioning

confidence: 99%

Controlling performance of semiactive suspension with two methods of fuzzy-control and machine-learning

Zhou,

Liu,

et al. 2024

Noise & Vibration Worldwide

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Two different methods of Fuzzy-control and Machine-learning are proposed to control the vehicle’s semiactive suspensions. To control performance of semiactive suspensions using machine learning (SS-ML) and semiactive suspensions using fuzzy control (SS-FC), a half-vehicle model has been established to calculate and simulate vibration equations under random road surfaces from ISO A-class to F-class. Via map of control rule data in SS-FC established at roads ISO A-class, B-class, …, and F-class, SS-ML’s Neuro-Adaptive-Learning has been trained for learning these control rules. The results obtained indicate that under the same road surface excitation of ISO C-class, the control performance of SS-FC and SS-ML is equivalent., and the vehicle’s comfort level using both SS-FC and SS-ML is very well improved in comparison with passive suspensions without control (PS-WC) of the vehicle. Under a mixed road surface from ISO A-class to F-class and a change range of the vehicle’s moving velocity from 2.5 m s−1 to 35m s−1 used for simulation, the vehicle’s comfort level using SS-ML is better than vehicle’s comfort level using SS-FC. Especially, the root mean square values of displacements and accelerations in vertical and pitch directions of the vehicle body with SS-ML are smaller than that of SS-FC by 13.4%, 23.2%, 20.7%, and 14.3%, respectively. Therefore, the control performance of SS-ML is better than SS-FC, and it should be used to control the vehicle’s semiactive suspensions for enhancing the comfort level.

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Development of Methodology for Semi Active Suspension System Using MR Damper

Cited by 9 publications

References 3 publications

Active and Passive Suspension System Performance under Random Road Profile Excitations

Active and Passive Suspension System Performance under Random Road Profile Excitations

Enhancing Suspension System Control Performance Using PID Controller Incorporated Low-Pass Filter Optimizated with Genetic Algorithm

Controlling performance of semiactive suspension with two methods of fuzzy-control and machine-learning

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