Longitudinal wheel slip control using dynamic neural networks

Ćirović, Velimir; Aleksendrić, Dragan; Smiljanic, Dusan

doi:10.1016/j.mechatronics.2012.11.007

Cited by 31 publications

(14 citation statements)

References 28 publications

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“…When one of the drive systems fails suddenly, the instantaneous generated additional yaw moment makes the vehicle bending greatly from the original running direction as shown in Figure 6. Until the additional yaw moment can be counterbalance by the following generated tire lateral force, the yaw rate might be maintain a constant value and the vehicle is traveling along a fixed radius turn [15]. The bending of the vehicle after the motor failure has been decreased by using the torque interception method.…”

Section: Transient Instability Mechanismmentioning

confidence: 99%

Brake Compensation Control for In-wheel Motor Drive System Failure

Zhang¹,

Zhang²,

Zhang³

et al. 2017

Proceedings of the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)

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Abstract. The in-wheel-motor drive electric vehicle has the advantages of compact structure, high efficiency and easy controllability. But once all of the unilateral drive systems are failure in the strong driving situation, the vehicle will be transient instability under the additional yaw moment produced by the torque of the normal driving wheels. To solve the above problem, the vehicle stability simulation analysis in the situations of unilateral drive system failure was carried out, the coupling relationship between the torque output from the drive systems and the vehicle stability was determined, and the dynamics mechanism of the vehicle transient instability was revealed. On the basis of the decoupling of the vehicle plane stability control parameters, an active yaw moment control strategy based on the electro-hydraulic composite braking compensation in the vehicle instability critical region was developed, and the simulation verification of the failure control was completed. The brake compensation control strategy is able to take the initiative to coordinate the torque coupling characteristics between the in-wheel motors and the hydraulic braking system, which ensures the vehicle stability after the failure of unilateral drive systems.

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Section: Transient Instability Mechanismmentioning

confidence: 99%

Brake Compensation Control for In-wheel Motor Drive System Failure

Zhang¹,

Zhang²,

Zhang³

et al. 2017

Proceedings of the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)

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show abstract

“…Taking into consideration that very complex and highly non-linear phenomena are involved in the braking process, a suffi ciently accurate analytical model of brake performance is almost impossible to obtain [245][246][247][248][249]. The reason for that lies in the fact that the coeffi cient of friction of a brake friction pair has to be considered as a non-linear parameter.…”

Section: Neuro-genetic Optimization Of the Performance Of A Brake Frimentioning

confidence: 99%

Composite materials – modelling, prediction and optimization

Aleksendrić

Carlone

2015

Soft Computing in the Design and Manufacturing of Composite Materials

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“…This method has been used, among others, in [6] to build a hybrid vehicle power management strategy, in [7] to model Diesel engine gas emissions, in [8] to estimate the position of an autonomous vehicle, in [9] for a longitudinal wheel slip controller, in [10] to monitor a Racecar driver vital parameters.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Vehicle Side-Slip Angle Using an Artificial Neural Network

Chindamo

Gadola

2018

MATEC Web of Conferences

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Abstract. In this work, a reliable and effective method to predict the vehicle side-slip angle is given by means of an artificial neural network. It is well known that artificial neural networks are a very powerful modelling tool. They are largely used in many engineering fields to model complex and strongly non-linear systems. For this application, the network has to be as simple as possible in order to work in real-time within built-in applications such as active safety systems. The network has been trained with the data coming from a custom manoeuvre designed in order to keep the method simple and light from the computational point of view. Therefore, a 5-10-1 (input-hidden-output layer) network layout has been used. These aspects allow the network to give a proper estimation despite its simplicity. The proposed methodology has been tested by means of the CarSim ® simulation package, which is considered one of the reference tools in the field of vehicle dynamics simulation. To prove the effectiveness of the method, tests have been carried out under different adherence conditions.

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Longitudinal wheel slip control using dynamic neural networks

Cited by 31 publications

References 28 publications

Brake Compensation Control for In-wheel Motor Drive System Failure

Brake Compensation Control for In-wheel Motor Drive System Failure

Composite materials – modelling, prediction and optimization

Estimation of Vehicle Side-Slip Angle Using an Artificial Neural Network

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