Due to manufacturing, assembly, and actuator wear, slight deviations between the
actual and logical positions of various gears in a transmission system may
accumulate, affecting shift quality, reducing shift accuracy, and causing
operational anomalies. To address this issue, a self-learning method based on
the top dead center (TDC) and lower dead center (LDC) was proposed, specifically
for the hybrid gearbox of an electric torque converter (eTC) module and a
double-input shaft gearbox (DIG). The linear active disturbance rejection
control (LADRC) method was employed to estimate and manage the nonlinear
resistance during the motion of the shifting motor. To simplify the controller
parameter problem, the nutcracker optimization algorithm (NOA) was utilized to
tune the LADRC parameters, thereby optimizing the position self-learning
process. The control strategy was modeled using MATLAB/SIMULINK, and its
reasonableness was verified through hardware-in-the-loop (HIL) tests. Based on
these tests, the approach was applied to three controllers: the PID controller,
LADRC, and NOA_LADRC. Subsequent gearbox bench experiments showed that the
self-learning method successfully corrected gear positions during product launch
and shifting. Among these controllers, NOA_LADRC effectively addresses nonlinear
disturbances, reducing the time required for identifying the shift drum position
by 0.06 s and 0.36 s, respectively. It provides critical parameters for the
control of the shift actuator, thereby optimizing shift performance and
indirectly enhancing overall performance.
