A soft computing approach to fuzzy sky-hook control of semiactive suspension

Caponetto, Riccardo; Diamante, O.; Fargione, G.; Risitano, Giacomo; Tringali, D.

doi:10.1109/tcst.2003.819592

Cited by 51 publications

(18 citation statements)

References 18 publications

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“…(see e.g. Caponetto et al (2003); Giua et al (2004); Geurts et al (2006); Liao and Wang (2003); Spelta et al (2009);Codeca et al (2007); Chrzan and Carlson (2001) ;Choi et al (2000); Deprez et al (2005)). …”

Section: Motivations and Frameworkmentioning

confidence: 99%

Survey and performance evaluation on some automotive semi-active suspension control methods: A comparative study on a single-corner model

Poussot-Vassal

Spelta

Sename

et al. 2012

Annual Reviews in Control

150

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In this paper, an overview and a benchmark of some semi-active suspension control strategy performances is proposed. Based on a recent result of the authors, where the optimal semi-active performance trade-off was addressed, here, a complete benchmark to evaluate any controlled semi-active suspensions is proposed, and applied on different control approaches. The present paper aims at providing a picture -as complete as possible -of the present state of the art in the semi-active suspension control field in terms of comfort and road-holding performance evaluation and trade-off.

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Section: Motivations and Frameworkmentioning

confidence: 99%

Survey and performance evaluation on some automotive semi-active suspension control methods: A comparative study on a single-corner model

Poussot-Vassal

Spelta

Sename

et al. 2012

Annual Reviews in Control

150

View full text Add to dashboard Cite

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“…The design of the active suspension requires determining a suitable control law u(·) for system (1). To this end, we first determine the control law u(·) that minimizes a performance index of the form…”

Section: Target Active Control Lawmentioning

confidence: 99%

“…In plot (g) we have reported the evolution of the first state variable x 1 , while in plot (h) we have reported the evolution of its error estimate e 1 = x 1 −x 1 . Figure (i) shows the evolution ofẋ 1 , whileė 1 =ẋ 1 −ẋ 1 is reported in plot (j).…”

Section: Simulationmentioning

confidence: 99%

Design of a Predictive Semiactive Suspension System

Giua

Melas

Seatzu

et al. 2004

Vehicle System Dynamics

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SUMMARYIn this paper we present an original design procedure for semiactive suspension systems. Firstly, we consider a target active control law that takes the form of a feedback control law. Secondly, we approximate the target law by controlling the damper coefficient f of the semiactive suspension. In particular, we examine two different kinds of shock absorbers: the first one uses magneto-rheological fluid instead of oil, while the second one is a solenoid valve damper. In both cases the nonlinear characteristics force-velocity of the damper are used to approximate the target law. To improve the efficiency of the proposed system, we take into account the updating frequency of the coefficient f and compute the expected value of f using a predictive procedure. We also address the problem of designing an asymptotic state observer that can be used not only to estimate the current state but also to predict the value that the state will take at the next sampling time.

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“…In addition, the control methods of the semi-active suspension system have shown competitive performance to those of the fully active suspension system and maintain the reliability of the passive suspension control methods. These advantages of the semi-active suspension systems have supported the use of the semi-active suspension system in commercial vehicles [4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Since the introduction of the skyhook control by Karnopp et al [4], a number of innovative control methodologies have been proposed to implement this strategy [5,[7][8][9][10]. Hong et al [5] proposed a road adaptive modified skyhook control for the semi-active Macpherson strut suspension system of hydraulic type.…”

Section: Introductionmentioning

confidence: 99%

Road-frequency adaptive control for semi-active suspension systems

Nguyen

Hong

Park

2010

Int. J. Control Autom. Syst.

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In this paper, a road-frequency adaptive control for semi-active suspension systems is investigated. The control aims to improve the vehicle suspension performance (ride comfort and wheel handling) for all frequency regions of road disturbances. In order to achieve this aim, the control law is extended from the conventional skyhook control, and the controller gains are scheduled for various frequency regions of road disturbances. By using the data measured from a relative displacement sensor, a state estimator based on a Kalman filter for estimating the required state variables is designed. Road disturbance frequencies are estimated by using a first order zero-crossing algorithm. The efficiency of the proposed control is shown through numerical simulations.

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A soft computing approach to fuzzy sky-hook control of semiactive suspension

Cited by 51 publications

References 18 publications

Survey and performance evaluation on some automotive semi-active suspension control methods: A comparative study on a single-corner model

Survey and performance evaluation on some automotive semi-active suspension control methods: A comparative study on a single-corner model

Design of a Predictive Semiactive Suspension System

Road-frequency adaptive control for semi-active suspension systems

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