Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Mechanical and Electrical Technology V |
| Pages | 272-276 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2013 Oct 29 |
| Event | 5th International Conference on Mechanical and Electrical Technology, ICMET 2013 - Chengdu, China Duration: 2013 Jul 20 → 2013 Jul 21 |
Publication series
| Name | Applied Mechanics and Materials |
|---|---|
| Volume | 392 |
| ISSN (Print) | 1660-9336 |
| ISSN (Electronic) | 1662-7482 |
Other
| Other | 5th International Conference on Mechanical and Electrical Technology, ICMET 2013 |
|---|---|
| Country | China |
| City | Chengdu |
| Period | 13-07-20 → 13-07-21 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
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Neural network approach to gain scheduling for traction control of electrical vehicles. / Young, Jieh Shian; Lin, Yi Pin; Shih, Po Wen.
Mechanical and Electrical Technology V. 2013. p. 272-276 (Applied Mechanics and Materials; Vol. 392).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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
PY - 2013/10/29
Y1 - 2013/10/29
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
UR - http://www.scopus.com/inward/citedby.url?scp=84886294449&partnerID=8YFLogxK
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
ER -