Transitional control of gear shift using estimation methods of drive torque without a turbine speed sensor

Ibamoto, Masahiko; Uchida, Makoto; Kuroiwa, Hiroshi; Minowa, Toshimichi; Sato, Kazuhiko

doi:10.1016/s0389-4304(96)00061-6

Cited by 6 publications

(1 citation statement)

References 3 publications

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“…It should be noted that u being a scalar is just a simplifying assumption that does not detract from the generality of the result, but it relates better to the application for our system. For the system shown above, u takes only discrete values (determined by the state vector, x), 49) and for each of these values there exists a corresponding flow vector, i.e.…”

Section: State-space Averagingmentioning

confidence: 99%

Robust nonlinear estimation and control of clutch-to-clutch shifts

Mishra

Srinivasan

2016

2016 American Control Conference (ACC)

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Shift quality is an integral component of drivability. Thus, with the extension of feedback control to more powertrain functions, it is of paramount importance that systematic and robust control methods be developed for ensuring good shift quality. Increasingly, automatic transmissions employ a combination of two actively controlled clutches to implement shifts, which makes the gearshifts clutch-to-clutch shifts. In the current study, a model-based approach is presented for the development of a robust clutch-to-clutch shift controller. A detailed nonlinear model of the powertrain consisting of engine, torque converter, transmission system, and vehicle dynamics is developed. The transmission system consists of mechanical and hydraulic subsystems. While the mechanical subsystem is easier to model, the hydraulic subsystem, commonly referred to as shift hydraulic system, is usually challenging to model due to high dynamic order and significant nonlinearities. Starting from a high order nonlinear model of the shift hydraulic dynamics, developed from first principles in an earlier study, we apply systematic methods to arrive at a lower order control-oriented model, and use it to develop a closed loop clutch pressure controller. Specifically, state-space averaging and singular perturbation techniques were used to achieve this. The reduced order model was validated against experimental measurements.

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Section: State-space Averagingmentioning

confidence: 99%