Self-learning adaptive control method and system for AMT system gear recognition references

Abstract

The invention relates to a self-learning adaptive control method and system for AMT system gear recognition references. The method mainly includes the steps of self-learning condition recognition and judging self-learning is required or not according to values of gear sensors; controlling the self-learning process, namely controlling gear shift actions according to action timing, repeating the gear shift actions many times, and selecting an optimal result as a self-learning result; recognizing the self-learning result, namely judging whether self-learning succeeds or not according to the self-learning result; making an adaptive learning strategy, namely when self-learning succeeds, adaptively updating recognition references of the gear sensors with the self-learning result which is judged being successful as a reference. Therefore, the method and system is efficient, practical and accurate, false self-learning results can be avoided effectively, and the self-learning results can be corrected quickly and accurately.

Description

technical field [0001] The invention relates to a self-learning and self-adaptive control method and system for a gear position discrimination standard of a mechanical automatic transmission (namely AMT). Background technique [0002] In recent years, with the rapid development of automotive electronic technology and modern control technology, the application of automotive automatic transmissions has become more and more extensive. At present, there are mainly the following types of automatic transmissions: torque converter automatic transmission (AT), continuously variable transmission (CVT), dual clutch automatic transmission (DCT) and mechanical automatic transmission (AMT). Based on the traditional manual transmission, AMT is transformed and upgraded by adding gear selection actuator, clutch actuator and transmission electronic control unit (TCU). [0003] Compared with other automatic transmissions, AMT has many advantages such as high transmission efficiency, low cost...

AI Technical Summary

Problems solved by technology

However, the existing AMT gear self-learning method cannot identify the top tooth phenomenon.

In this case, wrong self-learning results are often provided, resulting in the lack of normal safety diagnostic functions of AMT

At the same time, due to the change of external factors, the gear position discrimination standard sometimes has a large drift change, and the self-learning method lacking good adaptive characteristics at this time will lead to the lack of high reliability of the gearbox as a whole.

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