Data-driven predictive control for DCT vehicles starting process
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Abstract:
Aiming at the problems of modeling the starting process of dual clutch automatic transmission (DCT) vehicles and uncertain parameters, a data-driven predictive control method (DDPC) based on input-output data is proposed. Firstly, the starting process of DCT is equivalent to the autoregressive moving average exogenous model (ARMAX). Based on the input and output data of the system, the data-driven modeling process is implemented using the least square method. The validity of the modeling method is verified based on the MATLAB/Simulink platform. Secondly, combining the obtained ARMAX model with the proposed control approach, multiple groups of simulation analysis in different intentions are conducted. The results show that the proposed starting control strategy can well control the starting process and effectively reflect the driver's intention. Compared with the conventional constant engine speed control method, the proposed control method can effectively improve the starting performance. Also, the proposed control approach can well control the starting process under the changed starting condition, which proves that it is robust to a certain extent.
