Data-Driven Predictive Gearshift Control for Dual-Clutch Transmissions and FPGA Implementation

During the gearshift management process, the shift time and the shift shock affect the shift quality (smoothness and efficiency) greatly. In this paper, a gearshift controller is designed using a data-driven predictive control technique to improve the shift quality of vehicles with a dual-clutch transmission (DCT). It is directly obtained from the input-output data of a DCT model constructed by the commercial software AMESim. In order to obtain an offset-free control for the reference input, a predictor equation is gained with the incremental input and outputs. The conflicting control requirements of a short shift time and a small shift shock are taken into account in the optimal objective function by smoothly tracking a selected proper reference trajectory and limiting the change rate of the actuator. Moreover, due to physical characteristics, the constraints of the actuator are explicitly considered in the problem formulation. Finally, the effectiveness of the proposed data-driven predictive controller is tested in the AMESim simulation model of a DCT vehicle. Aiming to pursue a real-time improvement of the hardware computing speed for the data-driven predictive controller, a novel scheme for implementing the designed controller based on a field-programmable gate array (FPGA) is proposed. Moreover, a rapid prototyping platform based on an FPGA and dSPACE is introduced for testing and verifying the computational performance of the proposed controller. The experimental results show that the designed gearshift control algorithm for gearshift has better performance in real time, and the control performance can be guaranteed in a real-time environment.