Linear Quadratic Regulator and Fuzzy controller application in full-car model of suspension system with Magnetorheological shock absorber

Jahromi, Ali Fellah; Zabihollah, Abolghassem

doi:10.1109/mesa.2010.5552010

Cited by 27 publications

(14 citation statements)

References 4 publications

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“…The first term on the right-hand side of the equation 5accounts for the error between the initial and final state and second term accounts for the expenditure of the energy of the control signal. The matrices Q and R determine the relative importance of the error and expenditure of the performance index [18,19,20,34,35] (For detailed controller design refer [18,19]).…”

Section: Lqr Controllermentioning

confidence: 99%

Multi-objective optimization of LQR control quarter car suspension system using genetic algorithm

Nagarkar¹,

Patil²

2016

FME Transaction

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In this paper, genetic algorithm (GA) based multi-objective optimization technique is presented to search optimum weighting matrix parameters of linear quadratic regulator (LQR). Macpherson strut suspension system is implemented for study. GA is implemented to minimize vibration dose values (VDV), RMS sprung mass acceleration, sprung mass displacement and suspension working space. Constraints are put on RMS sprung mass acceleration, maximum sprung mass acceleration, tyre deflection, unsprung mass displacement and RMS control force. Passive suspension system and LQR control active suspension system are simulated in time domain. Results are compared using class E road and vehicle speed 80 kmph. For step response, GA based LQR control system is having minimum oscillations with good ride comfort. VDV is reduced by 16.54%, 40.79% and 67.34% for Case I, II and III respectively. Same trend is observed for RMS sprung mass acceleration. Pareto-front gives more flexibility to choose optimum solution as per designer's need.

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Section: Lqr Controllermentioning

confidence: 99%

Multi-objective optimization of LQR control quarter car suspension system using genetic algorithm

Nagarkar¹,

Patil²

2016

FME Transaction

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“…Vibration control of passenger car utilizing half-car was discussed by Yahaya et al [3]. To improve the accuracy of car model Fellah Jahromi and Zabihollah [4] modeled the vehicle suspension system by full car model; in which the accuracy of model improved component to quarter-car and half-car models.…”

Section: Fig 1 Schematic Model Ofmr Dampermentioning

confidence: 99%

“…MXS -k1x1 -k2x2 -k3X3 -k4x4 + (k1 + k2 +k3 + k4 + ks + k6 + k7 + ks)xs -kSX6 -k6X7 -k7XS -kSX9 + (k1'il + k2r21 -k3'31 -k4r41 + kS�l +k6r61 -k7r71 -kSI81)!P + (k1'i2 -k2r22 + k3'32 -��+��-��+��-��-�� -b2x2 -b3 X3 -b4x4 + (b1 + b2 + b3 + b4 + bs +b6 + b7 + bs )xs -bs\ -b6x7 -b7xs -bSX9 (4,4);1,1,1,1; zeros (4 , 4);…”

Section: Fullcarmodelmentioning

confidence: 99%

Linear quadratic Gaussian application and clipped optimal algorithm using for semi active vibration of passenger car

Zareh¹,

Sarrafan²,

Jahromi³

et al. 2011

2011 IEEE International Conference on Mechatronics

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A novel semi active control system for eleven Degrees of Freedom passenger car's suspension system using Magneto rheological damper is presented. The considered suspension system is modeled using mass spring damper model. The semi active vibration control is designed to reduce the amplitude of vehicle's vibration due to by road profile. The applied control strategies are based on a Linear Quadratic Gaussian optimal control algorithm to obtain desired damping forces of MR dampers. In considered system, the damping coefficient of the shock absorber changes actively through inducing magnetic field. The necessary input voltage to MR dampers to generate desired damping forces are derived using inverse dynamic model of MR damper using clipped optimal model. It is observed that the amplitude of vibration response can be significantly reduced using proposed semi active control scheme.

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“…Therefore, active suspension systems control has attracted the attention of numerous researchers interested in ride and holding qualities. However, active vehicle suspension system models assume a small displacement about an operating point, and then it creates a linearized working model [4][5][6]. On the other hand, the system states exhibit large deviation from the equilibrium point when the system is subjected to major impact owing to rough roads or aggressive driving.…”

Section: Introductionmentioning

confidence: 99%

A Robust Predictive Control Design for Nonlinear Active Suspension Systems

Bououden¹,

Chadli²,

Karimi³

2015

Asian Journal of Control

100

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This paper proposes a novel method for designing robust nonlinear multivariable predictive control for nonlinear active suspension systems via the Takagi‐Sugeno fuzzy approach. The controller design is converted to a convex optimization problem with linear matrix inequality constraints. The stability of the control system is achieved by the use of terminal constraints, in particular the Constrained Receding‐Horizon Predictive Control algorithm to maintain a robust performance of vehicle systems. A quarter‐car model with active suspension system is considered in this paper and a numerical example is employed to illustrate the effectiveness of the proposed approach. The obtained results are compared with those achieved with model predictive control in terms of robustness and stability.

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Linear Quadratic Regulator and Fuzzy controller application in full-car model of suspension system with Magnetorheological shock absorber

Cited by 27 publications

References 4 publications

Multi-objective optimization of LQR control quarter car suspension system using genetic algorithm

Multi-objective optimization of LQR control quarter car suspension system using genetic algorithm

Linear quadratic Gaussian application and clipped optimal algorithm using for semi active vibration of passenger car

A Robust Predictive Control Design for Nonlinear Active Suspension Systems

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