Pseudo-spectral optimization and data-driven control of vehicle start process with dual-clutch transmission

Abstract

• Real-time trajectory planning based on deep network and adaptive pseudo-spectral. • Data-driven predictive control method with dynamic parameter updating is proposed. • Effectively respond to time-varying start intentions in real time. • Real-time adaptation to the system uncertainty and nonlinear characteristics. • In simulations and experiments, it shows good control effect. In this study, a dual-clutch transmission start control strategy based on pseudo-spectral optimization and data-driven control is proposed to respond to the time-varying start intentions, to reduce friction and jerk, and to improve the start quality. First, taking the jerk, start time, and friction work as the optimization indexes, the adaptive pseudo-spectral method is used to accurately determine the optimal clutch engagement target trajectory. Then, a real-time planning method for the target clutch engagement trajectory is proposed based on a dual deep gated recurrent unit network. Second, through partial least squares identification, a data-driven predictive control (DDPC) method based on the autoregressive with exogenous model is proposed. The model parameters are updated online by a multi-model filtering algorithm based on input and output data, allowing real-time adaptation to the system uncertainty and nonlinear characteristics. Simulations and experiments show that the proposed target clutch trajectory planning method can effectively respond to time-varying start intentions in real-time. In addition, the DDPC controller can adapt to the uncertainty and nonlinear characteristics of the system and, hence, improve the control accuracy.