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Autoregressive Moving Average Exogenous Model-Based Adaptive Model Predictive Control for Dual-Clutch Transmission Starting Process
ISSN: 1946-4614, e-ISSN: 1946-4622
Published June 08, 2020 by SAE International in United States
Citation: Yang, Y., Wang, M., Qin, D., and Feng, J., "Autoregressive Moving Average Exogenous Model-Based Adaptive Model Predictive Control for Dual-Clutch Transmission Starting Process," SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 12(2):143-162, 2020, https://doi.org/10.4271/07-12-02-0011.
To address the difficulties in modeling the starting process of dual-clutch transmission (DCT) vehicles and poor adaptability of vehicles in complex driving conditions, this article proposes a new modeling and control strategy for the DCT starting system based on data-driven autoregressive moving average exogenous (ARMAX) modeling. Firstly, the DCT starting process is considered equivalent to the time series-related ARMAX model, and a data-driven ARMAX model could be obtained using input-output data relating to the starting process; also, the effectiveness of the data-driven ARMAX modeling technique is verified using the starting test of a real vehicle. Secondly, a data-driven adaptive model predictive control (A-MPC) strategy, which synthetically considers driving intention and clutch engagement status, is proposed. Finally, in order to verify the proposed control strategy, simulation analysis is conducted in different intentions; the results show that the proposed control strategy could realize the starting control effectively, and reflect driving intention. Compared with model predictive control only considering driving intention, the proposed control strategy could improve starting performance in different intentions; also, compared with the conventional control method, the A-MPC can improve the starting performance.