Improved Sliding-Mode On-line Adaptive Position Control for AMT Clutch Systems Based On Neural Networks

Abstract

Evaluation criteria of automated transmission control algorithms are an essential research object under increasingly stringent emission requirements and demand for riding comfort. Model-based development and calibration can be employed to optimize the corresponding actuator control algorithm in the transmission control unit (TCU). In this paper, the challenge is to realize on-line adaptively optimization of the controller based on position trajectory. The clutch in an automated manual transmission system is taken as a case study and is considered as a nonlinear dynamic system with uncertainties and unknown external disturbances. A controller with accurate and rapid position trajectory tracking performance is crucial to finding out the correlation between the clutch position changes and the shift quality. Traditionally, the control parameters are manually tuned off-line via trial-and-error, which is time-consuming and often yield poor results. Besides, any controller with constant control parameters has limited ability to adapt to dynamic, real-world conditions. This study proposes a second-order sliding-mode position controller. The parameters are improved based on neural network, to control the clutch system to get better tracking performance on-line adaptive during the vehicle starting. Finally, the clutch controller is embedded into a Modelica® based vehicle model and verified through the Model-in-the-Loop(MiL) simulation. The simulation results demonstrate that the controller has better tracking performance and stronger robustness compared to the conventional controller and its parameters can be efficiently tuned online via neural networking as opposed to trial-and-error.