Optimization Control of CVT Clutch Engagement Based on MPC

As an important part of continuously variable transmission (CVT) vehicle power transmission system, drive, neutral and reverse (DNR) wet clutch has the function of transmitting or interrupting vehicle power. However, due to the complex and variable working conditions of the clutch, it is difficult to achieve precise control of the clutch by the traditional control strategy. To solve this problem, a clutch control optimization algorithm based on model predictive control (MPC) is proposed. In order to identify and track the driver's launching intentions, a driver's launching intentions recognition system based on fuzzy neural network (FNN) is designed. The impact degree and friction work are taken as the evaluation standard of clutch control. The clutch controller is designed by using MPC control strategy, and the control effect is compared with the adaptive fuzzy neural network (AFNN) strategy. Finally, the validity of the control strategy is verified by simulation model and vehicle test. The results show that compared with the AFNN control strategy, the MPC control strategy can effectively control the clutch engagement and improve the vehicle launching quality.

[1]  Wen Ge Da.  A Review On Continuously Variable Transmissions Control , 2010 .

[2]  Yulong Lei,et al.  Shift Quality Optimization Based on Assessment System for Dual Clutch Transmission , 2012 .

[3]  Pingkang Li,et al.  Optimal decoupled control for dry clutch engagement , 2013, 2013 American Control Conference.

[4]  Zhou Jin-cha.  Research on shift strategy of AMT vehicle based on engine torque and speed control , 2015 .

[5]  John Manyala,et al.  Gearshift Actuator Dynamics Predictions in a Dual Clutch Transmission , 2013 .

[6]  Chao Yang,et al.  Identification of a driver’s starting intention based on an artificial neural network for vehicles equipped with an automated manual transmission , 2016 .

[7]  Jay H. Lee,et al.  Model predictive control: Review of the three decades of development , 2011 .

[8]  Andreas Myklebust,et al.  Dry Clutch Modeling, Estimation, and Control , 2014 .

[9]  Dongpu Cao,et al.  System Modeling and Pressure Control of a Clutch Actuator for Heavy-Duty Automatic Transmission Systems , 2016, IEEE Transactions on Vehicular Technology.

[10]  Chunhua Zheng,et al.  Performance optimization of CVT for two-wheeled vehicles , 2011 .

[11]  Feng Wei,et al.  Up-shift control in wet double clutch transmission. , 2010 .

[12]  Jan Swevers,et al.  A two-level Iterative Learning Control scheme for the engagement of wet clutches , 2011 .

[13]  Bin Zhou,et al.  Vehicle creep control based on nonlinear robust method for dual-clutch transmission , 2017, J. Syst. Control. Eng..

[14]  Fei Meng,et al.  System Modeling, Coupling Analysis, and Experimental Validation of a Proportional Pressure Valve With Pulsewidth Modulation Control , 2016, IEEE/ASME Transactions on Mechatronics.

[15]  Vu Trieu Minh,et al.  Automatic control of clutches and simulations for parallel hybrid vehicles , 2012 .

[16]  Zhang Zhi-gang.  Study on Transmission Characteristics of Torque for AMT Clutch Engagement , 2011 .

[17]  Hu Che.  Design of the hydraulic control system for CVT clutch , 2010 .

[18]  Si Jianyu.  Launching Intelligent Control Strategy for Dual Clutch Transmission , 2012 .

[19]  Liu Jin-gan.  Simulation for impulsion inhibition perfomance of starting fuzzy control of a continuously variable transmission , 2013 .

[20]  K. Tripathi,et al.  Engagement Control of Automotive Clutch by Mechatronic System Using Pre-determined Force Trajectories , 2014 .