The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 19, 2019

Filed:

Aug. 31, 2016
Applicants:

Jatco Ltd, Fuji-shi, Shizuoka, JP;

Nissan Motor Co., Ltd., Yokohama-shi, Kanagawa, JP;

Inventors:

Toshimitsu Araki, Ebina, JP;

Seiji Kasahara, Atsugi, JP;

Hideshi Wakayama, Hadano, JP;

Hiroyasu Tanaka, Atsugi, JP;

Assignees:

JATCO LTD, Fuji-Shi, JP;

NISSAN MOTOR CO., LTD., Yokohama-Shi, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
F16H 61/14 (2006.01); F16D 48/06 (2006.01); F16H 61/00 (2006.01);
U.S. Cl.
CPC ...
F16H 61/143 (2013.01); F16D 48/06 (2013.01); B60Y 2300/421 (2013.01); F16D 2500/1045 (2013.01); F16D 2500/10412 (2013.01); F16D 2500/50251 (2013.01); F16H 2061/0087 (2013.01); F16H 2061/146 (2013.01);
Abstract

In a vehicle equipped with a torque converter () having a lock-up clutch (), learning control for obtaining a learning value (L_n) on the basis of meet-point information, with which the lock-up clutch () initiates torque transmission, is carried out. After acquiring the current learning detection value (M_n), a meet-point learning control unit () calculates the current detection error (E_n) on the basis of the difference between the current learning detection value (M_n) and the previous learning value (L_(n−1)) that is stored. When the current and previous detection errors (E_n) and (E_n−1) have the same plus/minus sign, the current learning value correction amount is set to a larger value if the absolute value |(E_n−1)| of the previous detection error (E_n−1) is large than if the absolute value |(E_n−1)| is small. The sum of the previous learning value (L_(n−1)) and the current learning value correction amount is set as the present learning value (L_n).


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