Modeling and control active suspension system for a quarter car model

Darus, Rosheila; Enzai, Nur Idawati Md

doi:10.1109/cssr.2010.5773718

Cited by 22 publications

(12 citation statements)

References 2 publications

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“…A study about semi-active, nonlinear and robust control related to 1D, half car and full car models was presented by Horvat [16]. A 2 DoF linear quarter car suspension system is modelled by various researchers [4,6,7,8,9,11,12,14]. The suspension system is modelled as lumped masses and linear springs and dampers.…”

Section: Macpherson Strut Quarter Car Modelmentioning

confidence: 99%

“…Zheng and Cheng. [4], Zhen et al [5] and Darus and Enzai [6] presented LQR control scheme to control an actuator in an active suspension system. The values of weight matrices were selected for control application.…”

Section: Introductionmentioning

confidence: 99%

“…Ismail et al [14] presented the nonlinear feedback control of linear quarter car suspension system along with the LQR controller. Either LQR weight matrices were arbitrarily chosen by the authors for ride control application [2,5,6,12,14] or the weight matrices parameters of a LQR controller are adjusted by trial and error till desired performance is achieved [9,10,13].…”

Section: Introductionmentioning

confidence: 99%

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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: Macpherson Strut Quarter Car Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Nagarkar¹,

Patil²

2016

FME Transaction

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“…Various mathematical models for suspension systems have been presented by researchers to ride and control applications. A 2 degrees of freedom (DoF) quarter car suspension system was implemented by various researchers (Yahaya et al,2000;Elmadany and Al-Majed 2001;Assadian, 2002;Tueest et al, 2009;Darus and Enzai, 2010;Nekoui and Hadavi, 2010;Isamil et al, 2012). The suspension system is modeled as lumped masses and linear springs and dampers.…”

Section: Introductionmentioning

confidence: 99%

“…Either LQR weight matrices were arbitrarily chosen by the authors for ride control application (Yahaya et al, 2000;Zhen et al, 2006;Tusset et al 2009;Darus and Enzai, 2010;Nekoui and Hadavi, 2010;Hsabullah and Faris, 2010;Ismail et al, 2012) or the weight matrices parameters of a LQR controller were adjusted by trial and error until desired performance was achieved (Oral et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Optimization of the linear quadratic regulator (LQR) control quarter car suspension system using genetic algorithm

Nagarkar¹,

Patil²

2016

ing.inv.

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In this paper, a genetic algorithm (GA) based in an optimization approach is presented in order to search the optimum weighting matrix parameters of a linear quadratic regulator (LQR). A Macpherson strut quarter car suspension system is implemented for ride control application. Initially, the GA is implemented with the objective of minimizing root mean square (RMS) controller force. For single objective optimization, RMS controller force is reduced by 20.42 % with slight increase in RMS sprung mass acceleration. Trade-off is observed between controller force and sprung mass acceleration. Further, an analysis is extended to multi-objective optimization with objectives such as minimization of RMS controller force and RMS sprung mass acceleration and minimization of RMS controller force, RMS sprung mass acceleration and suspension space deflection. For multi-objective optimization, Pareto-front gives flexibility in order to choose the optimum solution as per designer's need.Keywords: Genetic algorithm (GA), MacPherson strut, quarter car, linear quadratic regulator (LQR), optimization. RESUMENEn este artículo se presenta un algoritmo genético (GA) basado en un enfoque de optimización con el fin de encontrar los parámetros de la matriz de ponderación del regulador lineal cuadrático (LQR). Se implementa un sistema de suspensión Macpherson para la aplicación del control de amortiguación. Inicialmente el GA se implementa con el objetivo de minimizar la raíz cuadrada media (RMS) del controlador de fuerza. Para la optimización de un único objetivo, el controlador RMS de la fuerza se reduce en un 20,42 % con un ligero aumento en la aceleración RMS de la masa suspendida. Se observa equilibrio entre el control de fuerza y la aceleración de la masa suspendida. Además, el análisis se extiende a la optimización multiobjetivo con objetivos como la minimización del control RMS de la fuerza y de la aceleración debida a la masa suspendida y la minimización del control RMS de la fuerza, RMS surgida por la aceleración de la masa y la deflexión del espacio de la suspensión. Para la optimización multiobjetivo el Pareto-frontal facilita elegir la solución óptima según la necesidad del diseñador.Palabras clave: Algoritmo genético (AG), suspensión MacPherson, cuarto de coche, regulador lineal cuadrático (LQR), optimización.

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GA-Based Multi-objective Optimal Control of Nonlinear Quarter Car Suspension

Nagarkar¹,

Patil²

2016

Lecture Notes in Mechanical Engineering

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Modeling and control active suspension system for a quarter car model

Cited by 22 publications

References 2 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

Optimization of the linear quadratic regulator (LQR) control quarter car suspension system using genetic algorithm

GA-Based Multi-objective Optimal Control of Nonlinear Quarter Car Suspension

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