Abstract
Vehicle launching has an important influence on driving performance of the vehicle. For vehicles with dual clutch transmissions (DCT), the clutch torque control is the key to the launching control. Therefore, a data-driven control method for DCT launching process based on adaptive neural fuzzy inference system (ANFIS) is proposed. Firstly, the vehicle test data during launching process is collected and the optimal clutch torque is obtained based on multi-objective particle swarm optimization (MOPSO). Afterward, to learn the launching control rules from optimization results, the combination of neural network and fuzzy logic algorithm, referred to as an ANFIS, is established. The dataset of the optimized launching clutch torque is utilized to train the ANFIS controller. Finally, the simulation and test results show that the data-driven control can accurately learn the launching control rules from the optimality, thereby achieving the optimal control for different launching intentions.
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Acknowledgments
This work was supported in part by the National Science Foundation of China (No. U1764259), in part by National Key R&D Program of China (2019YFE0121300), and in part by Chongqing Fundamental Research and Frontier Exploration Project (No. cstc2019jcyj-msxmx0668).
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Yonggang Liu IEEE Senior Member, was born in Chongqing, China in 1982. He received the B.S and Ph.D. degrees in Automotive engineering from Chongqing University, Chongqing, China, in 2004 and 2010, while he was a joint Ph.D. of University of Michigan-Dearborn, MI, USA, from 2007 to 2009. Now he is a Professor and Doctoral supervisor, Dean Assistant with College of Mechanical and Vehicle Engineering, Chongqing University. His research interests mainly include optimization and control of intelligent Electric Vehicles power system, and integrated control of vehicle Automatic Transmissions.
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Zhao, G., Liu, Y., Zhai, K. et al. Research on intelligent launching control of dual clutch transmissions based on adaptive neural fuzzy inference system. J Mech Sci Technol 36, 3227–3237 (2022). https://doi.org/10.1007/s12206-022-0604-x
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DOI: https://doi.org/10.1007/s12206-022-0604-x