LQR controller for active car suspension

Sam, Yahaya Md; Ghani, M.R.H.A.; Ahmad, Nasarudin

doi:10.1109/tencon.2000.893707

Cited by 69 publications

(32 citation statements)

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“…The LQR part guarantees the closed loop stability with appropriate choice of weighting matrices and variables [19][20][21]. Since not all states parameters can be measured directly, an estimator is used for the entire state vector with respect to the plant's output.…”

Section: Linear Quadratic Gaussianmentioning

confidence: 99%

Improving Vehicle Ride and Handling Using LQG CNF Fusion Control Strategy for an Active Antiroll Bar System

Zulkarnain

Zamzuri

Sam

et al. 2014

Abstract and Applied Analysis

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This paper analyses a comparison of performance for an active antiroll bar (ARB) system using two types of control strategy. First of all, the LQG control strategy is investigated and then a novel LQG CNF fusion control method is developed to improve the performances on vehicle ride and handling for an active antiroll bar system. However, the ARB system has to balance the trade-off between ride and handling performance, where the CNF consists of a linear feedback law and a nonlinear feedback law. Typically, the linear feedback is designed to yield a quick response at the initial stage, while the nonlinear feedback law is used to smooth out overshoots in the system output when it approaches the target reference. The half car model is combined with a linear single track model with roll dynamics which are used for the analysis and simulation of ride and handling. The performances of the control strategies are compared and the simulation results show the LQG CNF fusion improves the performances in vehicle ride and handling.

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Section: Linear Quadratic Gaussianmentioning

confidence: 99%

Improving Vehicle Ride and Handling Using LQG CNF Fusion Control Strategy for an Active Antiroll Bar System

Zulkarnain

Zamzuri

Sam

et al. 2014

Abstract and Applied Analysis

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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%

“…The LQR control scheme to control an actuator in an active suspension system was presented where the critical issue was to select the weight matrices which in turn determine the system performance. The values of weighting matrices were selected for simulation (Yahaya et al, 2000;Zhen et al, 2006). Yoon et al had presented an application of optimal control law via an LQR control method for controlling altitude of the tri-rotor UAV.…”

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%

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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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“…According to the suspension system's ability to add or extract energy, they can be classified as passive, semi-active or active [6]. Passive suspension consists of conventional springs and dampers and it cannot add energy to the system.…”

Section: Introductionmentioning

confidence: 99%

VHDL-AMS Based Genetic Optimization of Mixed-Physical-Domain Systems in Automotive Applications

Wang

Kázmierski

2009

SIMULATION

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This paper presents a VHDL-AMS based genetic optimization methodology suitable for performance improvement of hardware systems in automotive applications. Models of such systems are mixedsignal (analog and digital) in which the analog parts cover mixed physical domains. A case study applying this novel method to the fuzzy logic controller (FLC) optimization in an automotive active suspension system (AASS) has been investigated. A new type of fuzzy logic membership functions with variable geometrical shapes has been proposed and optimized. In this optimization technique, VHDL-AMS is used not only for the modeling and simulation of the FLC and its underlying AASS but also for the implementation of a parallel genetic algorithm (GA). This has resulted in an integrated performance optimization system wholly implemented in the hardware description language (HDL). Results show that the proposed FLC has superior performance to that of existing FLCs that use fixed-shape membership functions.

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LQR controller for active car suspension

Cited by 69 publications

References 1 publication

Improving Vehicle Ride and Handling Using LQG CNF Fusion Control Strategy for an Active Antiroll Bar System

Improving Vehicle Ride and Handling Using LQG CNF Fusion Control Strategy for an Active Antiroll Bar System

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

VHDL-AMS Based Genetic Optimization of Mixed-Physical-Domain Systems in Automotive Applications

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