Optimal launching-intention-aware control strategy for automated clutch engagement

Clutch engagement control is critical during launching process in a vehicle equipped with an automated manual transmission (AMT), which is a problem including muti-objective optimization and nonlinear control. In this paper, a optimal launching-intention-aware control strategy is proposed for clutch engagement. Firstly, a launching-intention-aware machine (LIAM) based on artificial neural network (ANN) is designed to identify the driver’s launching intentions. Then the optimal laws of clutch engagement for different launching conditions are obtained based on the dynamic programming (DP), which regard friction loss, vehicle shock, angular acceleration of engine and engine torque as the optimizing indexes. Next, a slidingmode controller (SMC) is designed for the clutch engagement. Finally, the performances of the optimal laws and the SMC are validated by the joint simulation of Simulink-AMESim, and the results show that the requirements of vehicle launching are met. The proposed clutch engagement control strategy would provide a better theoretical support for the practical-extended application of AMT.

• Record URL:
  https://doi.org/10.1007/s12239-017-0042-4
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/12299138
• Supplemental Notes:
  • Copyright © 2017, The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg.
• Authors:
  • Yang, Yi-yong
  • Zhu, Zao-bei
  • Wang, Xiang-yu
  • Chen, Zheng
  • Ma, Zi-lin
• Publication Date: 2017-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 417-428
• Serial:
  • International Journal of Automotive Technology
  • Volume: 18
  • Issue Number: 3
  • Publisher: Korean Society of Automotive Engineers
  • ISSN: 1229-9138
  • EISSN: 1976-3832
  • Serial URL: http://link.springer.com/journal/12239

Subject/Index Terms

• TRT Terms: Automation; Clutches; Drivers; Dynamic programming; Manual transmissions; Neural networks; Optimization; Sliding mode control
• Uncontrolled Terms: Intentions
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01635541
• Record Type: Publication
• Files: TRIS
• Created Date: May 25 2017 1:56PM