A neural network based fuzzy control approach to improve ride comfort and road handling of heavy vehicles using semi-active dampers

Eslaminasab, Nima; Biglarbegian, Mohammad; Melek, William; Golnaraghi, M. F.

doi:10.1504/ijhvs.2007.013259

Cited by 22 publications

(17 citation statements)

References 28 publications

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“…Wang et al [12] proposed a PI control scheme for vibration attenuation of a quarter-vehicle model subject to idealized harmonic and transient base excitations. More recently, many control methods including neural network control [13], H-infinity control [7], fuzzy logic control [14,15], neural network-based fuzzy control [16], neuro-fuzzy control [17], discretetime fuzzy sliding mode control [18], optimal fuzzy control [19], adaptive fuzzy logic control [20,21] etc. have been explored.…”

Section: Introductionmentioning

confidence: 99%

Comparative research on semi-active control strategies for magneto-rheological suspension

et al. 2009

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This paper presents the comparison results of a study to identify an appropriate semi-active control algorithm for a MR suspension system from a variety of semi-active control algorithms for use with MR dampers. Five representative control algorithms are considered including the skyhook controller, the hybrid controller, the LQG controller, the sliding mode controller and the fuzzy logic controller. To compare the control performances of the five control algorithms, a quarter car model with a MR damper is adopted as the baseline model for our analysis. After deriving the governing motion equations of the proposed dynamic model, five controllers are developed. Then each control policy is applied to the baseline model equipped with a MR damper. The performances of each control algorithm under various road conditions are compared along with the equivalent passive model in both time and frequency domains through the numerical simulation. Subsequently, a road test is performed to validate the actual control performance. The results show that the performance of a MR suspension X. Dong ( ) system is highly dependent on the choice of algorithm employed, and the sliding mode control strategy exhibits an excellent integrated performance.

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Section: Introductionmentioning

confidence: 99%

Comparative research on semi-active control strategies for magneto-rheological suspension

et al. 2009

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“…The model was used for testing of skyhook and other strategies of semi active suspension system. Modeling of one and two DOF for a quarter car design a semi-active twin-tube shock has been developed by Esslaminasa et al [7]. Darus [8] adopted a state space approach in developing a mathematical model for both a quarter car and full car using MATLAB packages.…”

Section: Introductionmentioning

confidence: 99%

“…The parameters of suspension system and the definition of equation variables are summarized in Table 1 which was adopted from [7] and [14], and some variables were emended to meet the specifications of the vehicle used in the experiment. The system can be represented in state space matrix forms as: ̇ ( ) ( ) is the input state equation and ( ) ( ) output for the state equation.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Suspension Performance Analysis Based on Full Vehicle Model for Condition Monitoring Development

Hamed

Tesfa

et al. 2014

Mechanisms and Machine Science

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“…Yoshimura et al [21] proposed a semi-active controller using fuzzy reasoning. A neural-network-based fuzzy controller was also proposed by Eslaminasab et al [22] to improve the suspension performance of heavy-duty vehicles. These studies showed that artificial intelligence approaches are very favorable for semiactive suspension control.…”

Section: Introductionmentioning

confidence: 99%

A Noise-Insensitive Semi-Active Air Suspension for Heavy-Duty Vehicles with an Integrated Fuzzy-Wheelbase Preview Control

Xie

Wong

Zhao

et al. 2013

Mathematical Problems in Engineering

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Semi-active air suspension is increasingly used on heavy-duty vehicles due to its capabilities of consuming less power and low cost and providing better ride quality. In this study, a new low cost but effective approach, fuzzy-wheelbase preview controller with wavelet denoising filter (FPW), is developed for semi-active air suspension system. A semi-active suspension system with a rolling lobe air spring is firstly modeled and a novel front axle vertical acceleration-based road prediction model is constructed. By adopting a sensor on the front axle, the road prediction model can predict more reliable road information for the rear wheel. After filtering useless signal noise, the proposed FPW can generate a noise-insensitive control damping force. Simulation results show that the ride quality, the road holding, the handling capability, the road friendliness, and the comprehensive performance of the semi-active air suspension with FPW outperform those with the traditional active suspension with PID-wheelbase preview controller (APP). It can also be seen that, with the addition of the wavelet filter, the impact of sensor noise on the suspension performance can be minimized.

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A neural network based fuzzy control approach to improve ride comfort and road handling of heavy vehicles using semi-active dampers

Cited by 22 publications

References 28 publications

Comparative research on semi-active control strategies for magneto-rheological suspension

Comparative research on semi-active control strategies for magneto-rheological suspension

Vehicle Suspension Performance Analysis Based on Full Vehicle Model for Condition Monitoring Development

A Noise-Insensitive Semi-Active Air Suspension for Heavy-Duty Vehicles with an Integrated Fuzzy-Wheelbase Preview Control

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