Fault Estimation of Rack-Driving Motor in Electrical Power Steering System Using an Artificial Neural Network Observer

• Published in: Electronics (Basel) Vol. 11; no. 24; p. 4149
• Main Authors: Kim, Seulgi, Jung, Dae-Yi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Algorithms; Amplitudes; Artificial neural networks; Design; Electric fault location; Fault diagnosis; Investigations; Methods; Neural networks; Observers; Power steering; Simulation; Steering systems; South Korea
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11244149

In this paper, we present the fault estimation of a motor in rack-type electrical power steering (R-EPS) system using an artificial neural network (ANN) observer and the comparison study of estimation performance between ANN observer and model-based ones. For various amplitudes and frequencies of fault, it is not easy to obtain the accurate fault estimation using the model-based observers inherently possessing the accumulated errors and disturbance effect. Such model-based methods often result in undesired consequences; hence, this study employed the “model-free” ANN observer, without using any dynamics and parameters of a motor, to accomplish the better outcomes. Furthermore, the advantages of ANN observer for the fault estimation of the motor have been clearly investigated under several control/fault scenarios, and the effectiveness of proposed work has been validated through an actual experimental study. It is found that the performance of model-based approaches is degraded when the frequencies and amplitudes of fault and control scenarios are changed, but the ANN observer guaranteed performance that was almost the same regardless of fault and control scenarios. Notably, ANN observers showed 84% to 95% of estimation accuracy with almost no delay between estimates and actual faults while model-based approaches did 68% to 86% accuracy along with noticeable delay.