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Practicability study on the suitability of artificial, neural networks for the approximation of unknown steering torques.

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Bibliographic Details
Title: Practicability study on the suitability of artificial, neural networks for the approximation of unknown steering torques.
Authors: Van Ende, K. T. R.1, Schaare, D.1, Kaste, J.1, Küçükay, F.2, Henze, R.2, Kallmeyer, F. K.1
Source: Vehicle System Dynamics. Oct2016, Vol. 54 Issue 10, p1362-1383. 22p.
Subjects: Artificial neural networks, Computational steering (Computer science), Feedback control systems, Robust control, Program transformation
Abstract: For steer-by-wire systems, the steering feedback must be generated artificially due to the system characteristics. Classical control concepts require operating-point driven optimisations as well as increased calibration efforts in order to adequately simulate the steering torque in all driving states. Artificial neural networks (ANNs) are an innovative control concept; they are capable of learning arbitrary non-linear correlations without complex knowledge of physical dependencies. The present study investigates the suitability of neural networks for approximating unknown steering torques. To ensure robust processing of arbitrary data, network training with a sufficient volume of training data is required, that represents the relation between the input and target values in a wide range. The data were recorded in the course of various test drives. In this research, a variety of network topologies were trained, analysed and evaluated. Though the fundamental suitability of ANNs for the present control task was demonstrated. [ABSTRACT FROM PUBLISHER]
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Database: Engineering Source
Description
Abstract:For steer-by-wire systems, the steering feedback must be generated artificially due to the system characteristics. Classical control concepts require operating-point driven optimisations as well as increased calibration efforts in order to adequately simulate the steering torque in all driving states. Artificial neural networks (ANNs) are an innovative control concept; they are capable of learning arbitrary non-linear correlations without complex knowledge of physical dependencies. The present study investigates the suitability of neural networks for approximating unknown steering torques. To ensure robust processing of arbitrary data, network training with a sufficient volume of training data is required, that represents the relation between the input and target values in a wide range. The data were recorded in the course of various test drives. In this research, a variety of network topologies were trained, analysed and evaluated. Though the fundamental suitability of ANNs for the present control task was demonstrated. [ABSTRACT FROM PUBLISHER]
ISSN:00423114
DOI:10.1080/00423114.2016.1202987