基于知识的双离合器自动变速器换挡智能控制

Intelligent Knowledge-based Shifting Control of Dual Clutch Transmission

针对双离合器自动变速器(Dual clutch transmissions,DCT)在不同工况下的换挡过程控制问题,提出了一种基于知识的换挡控制策略。通过集成学习算法,将从实车数据中学习到的离合器目标转矩在线应用到DCT换挡过程中,并利用模型预测控制(Model predictive control,MPC)的误差反馈特性,减小系统真实转速与参考转速间的偏差,保证集成学习算法可根据传感器测得的转速准确输出目标转矩;根据DCT换挡过程动力学状态空间方程,建立MPC控制器的状态预测模型;构建换挡过程目标函数,基于MPC控制器对换挡控制量进行滚动优化;在Matlab/Simulink平台上分别对DCT升挡和降挡工况下的换挡过程进行仿真,并与传统的模糊控制和实车标定的控制方法进行对比分析。结果表明,基于知识的换挡控制能够准确挖掘并应用实车数据中潜在的换挡控制规律,实现了快速平顺换挡的目标,具备更优的换挡性能和更高的智能化水平。

Aiming at coping with the dual clutch transmission(DCT)shifting process control under different working conditions,a knowledge-based shifting control method is proposed in this paper.By implementing the ensemble learning algorithm,the clutch target torque generated based on the actual vehicle data is applied online to the DCT shift process.To ensure that the ensemble learning algorithm can accurately generate the target torque according to the speed measured by the sensor,model predictive control(MPC)is used to reduce the deviation between the real speed and the reference speed of the system.According to the state dynamic equation during DCT shifting process,the state prediction model of MPC controller is established.The objective function of shifting control is constructed with the constraints of state variables and control variables.Moreover,the rolling optimization of the shifting control is realized by MPC.The DCT upshift and downshift processes are simulated on the Matlab/Simulink co-simulation platform,respectively.The shifting quality of the proposed knowledge-based method is compared with the fuzzy control and the actual vehicle calibration control.The results show that the proposed knowledge-based shifting control method can apply the shifting control laws accurately from the actual vehicle data by data mining.Furthermore,the goal of fast and smooth shifting is achieved,which indicates that it is more intelligent shifting control and has better shifting performance.