Hybrid neural network model for history-dependent automotive shock absorbers

Pracny, V.; Meywerk, Martin; Lion, Alexander

doi:10.1080/00423110600810499

Cited by 19 publications

(13 citation statements)

References 6 publications

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“…This component represents mainly the frequency dependence and is described by a feed forward NN using one hidden layer with 10 neurons. The major benefit of this technique is high accuracy of approximation [4,5].…”

Section: Hybrid Shock Absorber Modelmentioning

confidence: 99%

“…The objective of the hybrid model [5] is to link the benefits and advantages of the spline approach and the NN modelling technique. By splitting the total damping force into the two components F d = F spl + F nn , two parallel forces are obtained (figure 4).…”

Section: Hybrid Shock Absorber Modelmentioning

confidence: 99%

“…For this purpose Akima splines have been applied. The spline component can be modified easily later on to enable parameter studies with regard to the investigation of the vehicle behaviour [5].…”

Section: Hybrid Shock Absorber Modelmentioning

confidence: 99%

“…Once the parameters are set, they refer to the system of interest and their selected manipulation is not allowed. This technology has been already used to model highly non-linear suspension components [2][3][4][5]. Although every study is using different input quantities, most of them are based on a multi-layer feed forward NN and the backpropagation learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Full vehicle simulation using thermomechanically coupled hybrid neural network shock absorber model

Pracny

Meywerk

Lion

2008

Vehicle System Dynamics

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In the case of full vehicle models, the technique of multi-body simulation (MBS) is frequently used to study their highly non-linear dynamic behaviour. Many non-linearities in vehicle models are induced by force elements like springs, shock absorbers, bushings and tires. Commonly, spline functions are used to represent the force responses of these components. If the non-linear relationships are more complicated, the spline approximations are no more accurate. An alternative approach is based on empirical neural networks which are based on the mathematical approximation of measured data. It is well known that neural networks are able to represent and predict complex component responses accurately. The aim of this paper is to perform a dynamic full vehicle simulation using a thermomechanically coupled hybrid neural network shock absorber model. In this shock absorber model, the spline approach is combined with a temperature-dependent neural network. Based on a displacement-controlled excitation on a four post test rig in the ADAMS/Car MBS software, a rugged test track is simulated. In this way, the front and rear shock absorbers are dynamically loaded with comfort-relevant frequencies in the range of 0.75-30 Hz and velocity amplitudes up to 2 m/s. By the simulation, stability of the hybrid neural network model is demonstrated. Furthermore, the damping force, the vertical acceleration of the chassis and the required simulation times are compared. The standard spline approach is used as a reference.

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Section: Hybrid Shock Absorber Modelmentioning

confidence: 99%

Section: Hybrid Shock Absorber Modelmentioning

confidence: 99%

Section: Hybrid Shock Absorber Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Full vehicle simulation using thermomechanically coupled hybrid neural network shock absorber model

Pracny

Meywerk

Lion

2008

Vehicle System Dynamics

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“…Neural networks have been effectively applied to model complex systems due to their good learning capability. The modelling is not based on any physical law and does not introduce any simplifying hypothesis concerning the physics governing the system (Pracny et al, 2007). propose a tyre modeling method in terms of multi-layer perceptron (MLP) neural network using a backpropagation algorithm.…”

Section: Introductionmentioning

confidence: 99%

Neural-empirical tyre model based on recursive lazy learning under combined longitudinal and lateral slip conditions

Boada¹,

García-Pozuelo²,

Díaz³

2011

Int.J Automot. Technol.

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The behaviour of the tyre plays an important role in the vehicle handling. An accurate tyre model that estimates these forces and moments it is highly essential for the studies of vehicle behaviour. For the last ten years neural networks have attracted a great deal of attention in vehicle dynamics and control. Neural networks have been effectively applied to model complex systems due to their good learning capability. In this paper a recursive lazy learning method based on neural networks is considered to model the tyre characteristics under combined braking and cornering. The proposed method is validated by comparison with experimental obtained responses. Results show the estimated model correlates very well with the data obtained experimentally. Moreover, the neural model proposed allows to include the asymetric tyre behaviour in the tyre model without difficulty.

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Hybrid neural network shock absorber model

Pracny¹,

Meywerk²

2007

Proc Appl Math and Mech

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A hybrid neural network model is presented and described. The model is composed of a mechanical and thermodynamical part. The mechanical part is described by Akima spline in combination with a feed-forward neural network, while in the case of the thermodynamic part differential equation of dissipative heating is formulated. The interface between both part is provided by the neural network. To identify a proper parameter set of the hybrid model a shock absorber of a middle class passenger car is measured on a servo-hydraulic testing machine. As an excitation a stochastic signal with a predefined power spectral density (PSD) is used. Subsequently the hybrid shock absorber is implemented into a full vehicle model in ADAMS/Car to test its numerical performance and influence on the vertical vehicle dynamics. As reference a standard spline shock absorber model is taken.

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Hybrid neural network model for history-dependent automotive shock absorbers

Cited by 19 publications

References 6 publications

Full vehicle simulation using thermomechanically coupled hybrid neural network shock absorber model

Full vehicle simulation using thermomechanically coupled hybrid neural network shock absorber model

Neural-empirical tyre model based on recursive lazy learning under combined longitudinal and lateral slip conditions

Hybrid neural network shock absorber model

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