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In the electro-pneumatic automated mechanical transmission (AMT) system, the manufacturing, assembly, wear, replacement and other issues often lead to gear position change and some differences in various gears of transmission, which reduces the success rate and even results in abnormally working. To solve these problems, based on the intelligent control theories of fuzzy PI control, this research developed an intelligent self-learning system for the AMT gear position. This system contains the position initialization module after assembling (offline module) and the position correction module in use (online module). The system can automatically recognize gear position deviation and actively correct it, which improves the robustness of shift actuator. The precise control of shift actuator is the key of this self-learning system.

Drag torque in multi-plate wet clutch consumes engine energy, but also has significant affect on synchronization process during shifting. The mathematical models of drag torque and shift synchronization process were established, and the results carried out by Matlab/Simulink simulation were compared with experiment results as well. It is demonstrated that the models are correct and accurate. The influences of friction pair gap in multi-plate wet clutch, cooling oil flow and properties of cooling oil under different temperature etc. on drag torque, shift synchronization time and energy were analyzed. The conclusions have certain guiding significance to the multi-plate wet clutch theoretical study and structural design as well as transmission system matching.