TY - GEN
T1 - Neural network approach to gain scheduling for traction control of electrical vehicles
AU - Young, Jieh Shian
AU - Lin, Yi Pin
AU - Shih, Po Wen
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - This paper proposes a gain scheduling approach by neural network to force control of the electric vehicle wheels. To approximate to the reality in simulation, we utilize the traction force database of the motor, called the current-RPM-torque database, instead of the slip ratio measurements. The system is nonlinear and a constant gain cannot overcome all road conditions of the traction force control for the electric vehicles. The appropriate gains for different road conditions can be the training data of the neural network. In this paper, the proper parameters for the RBF neural network are obtained. The appropriate gains which have to fit the assigned specifications in time domain seem to be inverse proportion to the slip ratio slope.
AB - This paper proposes a gain scheduling approach by neural network to force control of the electric vehicle wheels. To approximate to the reality in simulation, we utilize the traction force database of the motor, called the current-RPM-torque database, instead of the slip ratio measurements. The system is nonlinear and a constant gain cannot overcome all road conditions of the traction force control for the electric vehicles. The appropriate gains for different road conditions can be the training data of the neural network. In this paper, the proper parameters for the RBF neural network are obtained. The appropriate gains which have to fit the assigned specifications in time domain seem to be inverse proportion to the slip ratio slope.
UR - http://www.scopus.com/inward/record.url?scp=84886294449&partnerID=8YFLogxK
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U2 - 10.4028/www.scientific.net/AMM.392.272
DO - 10.4028/www.scientific.net/AMM.392.272
M3 - Conference contribution
AN - SCOPUS:84886294449
SN - 9783037858349
T3 - Applied Mechanics and Materials
SP - 272
EP - 276
BT - Mechanical and Electrical Technology V
T2 - 5th International Conference on Mechanical and Electrical Technology, ICMET 2013
Y2 - 20 July 2013 through 21 July 2013
ER -