Semi-Active Suspension with Preview Using a Frequency-Shaped Performance Index

Kim, Hyuk; Yoon, Yoo‐Seok

doi:10.1080/00423119508969117

Cited by 20 publications

(5 citation statements)

References 5 publications

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“…The equations of motion for the bounce, pitch and roll responses of the engine are: The equation of motion for the driver seat can be written as: (7) The bounce motion of the wheels is modeled as: (8) where; with i = 1, 2, 3, 4 (front left, rear left, front right and rear right body corners) and j = 1, 2, 3 (transmission mount, engine mount, and subfram mount)…”

Section: Vehicle Mathematical Modelmentioning

confidence: 99%

“…One of the most important findings in their study is that, the performance of the semi-active system with preview is as good as the performance of the active system without preview. Kim and Yoon [7] proposed a control law for a semi-active suspension with preview control for the purpose of ride quality improvement, which is determined by reproducing the control force of an optimally controlled active suspension while suppressing its damping coefficient variation. The performance index of the optimal control for the active suspension is modified to include frequency-shaping by use of Parseval's theorem, which allows deemphasizing the effects of particular variables over specific frequency bands.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Preview Enhanced Rule-Optimized Fuzzy Logic Damper Controller

Kaldas

Çalışkan

Henze

et al. 2014

SAE Int. J. Passeng. Cars - Mech. Syst.

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New developments in road profile measurement systems and in semi-active damper technology promote the application of preview control strategies to vehicle suspension systems. This paper details a new semi-active suspension control approach in which a rule-optimized Fuzzy Logic controller is enhanced through preview capability. The proposed approach utilizes an optimization process for obtaining the optimum membership functions and the optimum rule-base of the preview enhanced Fuzzy Logic controller. The preview enhanced Fuzzy Logic controller uses the feedforward road input information and the feedback vehicle state information as the controller inputs. An eleven degree of freedom full vehicle model, which is validated through laboratory tests performed on a hydraulic four-poster shaker, is used for the controller synthesis. The cost function including both ride comfort and road holding performance of the full vehicle is minimized through a discrete optimization process utilizing Genetic Algorithm (GA). The preview distance is also considered as a design parameter during the optimization process. The performance of the preview enhanced rule-optimized Fuzzy Logic controller is evaluated by using a measured stochastic road profile as vehicle model input. The results demonstrate the potential of the preview enhanced controller in improving all aspects of system performance compared to the ruleoptimized Fuzzy Logic controller without preview.

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Section: Vehicle Mathematical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preview Enhanced Rule-Optimized Fuzzy Logic Damper Controller

Kaldas

Çalışkan

Henze

et al. 2014

SAE Int. J. Passeng. Cars - Mech. Syst.

View full text Add to dashboard Cite

show abstract

“…For constructing the preview information, an observer was presented by Huisman et al in [17], such that no model for the road surface is needed. A frequency shaped performance index in the optimal control problem was introduced by Kim and Yoon in [18], which has shown a significant improvement with respect to ride comfort. Kitching et al investigated the benefits of wheel-base preview experimentally in [19] by having a simulation model running ahead to compensate the lagging phase of the control force.…”

Section: Introductionmentioning

confidence: 99%

Preview control of semi-active suspension based on a half-car model using Fast Fourier Transform

Ahmed

Svaricek

2013

10th International Multi-Conferences on Systems, Signals &Amp; Devices 2013 (SSD13)

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In this paper, a preview controller is proposed such that, the road profile in front of the vehicle is scanned using lidar sensors. The measured sensor signal is analyzed using Fast Fourier Transform FFT. According to the amplitude and frequency of the signal components, a control signal is generated based on Model Predictive Control (MPC), which is calculated offline using a half-car model. The system performance is compared with the passive suspension, the Skyhook approach, an adaptive robust controller, and an optimal preview controller. The simulation and experimental results have shown a significant better performance regarding ride comfort.Index Terms-Predictive control, vibration control, control of vehicle suspension.

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“…Some researchers have used model reference adaptive control to reflect the uncertainties available in the vehicle model (Esmailzadeh and Fahimi, 1997;Sunwoo, Cheok, and Huang, 1991; Bakhtiari-Nejad and Karami-Mohammadi, 1998). Frequency-shaped performance index has also been proposed to de-emphasize the effects of particular variables over specific frequency bands (Kim and Yoon, 1995). Some &dquo;second-generation&dquo; approaches to active and semiactive suspension control problems were addressed by Gordon et al (1994).…”

Section: Introductionmentioning

confidence: 99%

Predictive Time-Delay Control of Vehicle Suspensions

Vahidi

Eskandarian

2001

Journal of Vibration and Control

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A predictive control methodology is introduced for active vehicle suspension control, taking into account the effect of time delay as an inherent characteristic of active systems in the absence of preview information. In this method, at each time instant, the control signal is calculated by minimizing a perfor mance index, based on the predicted response in a finite prediction horizon. The discrete predictive method ology, compared with the widely used linear quadratic regulator method, is easier to formulate and results in a discrete-time control algorithm suitable for digital control, and time delays can be more easily introduced in the formulation. In this paper, predictive control simulation results are compared with passive control re sults. It is shown that this method is capable of improving ride performance of the single-degree-of-freedom quarter-car model for a wide range of input frequencies in the presence of time delay, while in the meantime it maintains the maximum suspension travel below the maximum of passively controlled suspensions.

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Semi-Active Suspension with Preview Using a Frequency-Shaped Performance Index

Cited by 20 publications

References 5 publications

Preview Enhanced Rule-Optimized Fuzzy Logic Damper Controller

Preview Enhanced Rule-Optimized Fuzzy Logic Damper Controller

Preview control of semi-active suspension based on a half-car model using Fast Fourier Transform

Predictive Time-Delay Control of Vehicle Suspensions

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