See discussions, st ats, and author pr ofiles f or this public ation at : https://www .researchgate.ne t/public ation/332156126 Modeling, Control, and Adaptation for Shift Quality Control of Automatic Transmissions Conf erence Paper · April 2019 DOI: 10.4271/2019-01-1129 CITATIONS 0READS 85 3 author s, including: Some o f the author s of this public ation ar e also w orking on these r elat ed pr ojects: Robust c ontr ol of clut ch t o clut ch g earshifts View pr oject Torque c onv erter identific ation and c ontr ol View pr oject Kirti Deo Mishr a The Ohio St ate Univ ersity 7 PUBLICA TIONS    15 CITATIONS     SEE PROFILE Krishnasw amy Sriniv asan The Ohio St ate Univ ersity 84 PUBLICA TIONS    1,391 CITATIONS     SEE PROFILE All c ontent f ollo wing this p age was uplo aded b y Krishnasw amy Sriniv asan on 28 Mar ch 2020. The user has r equest ed enhanc ement of the do wnlo aded file.2019-01-1129 Published 0 2 Apr 2019 © 2019 SAE International. All Rights Reserved.Modeling, Control, and Adaptation for Shift Quality Control of Automatic Transmissions Kirti Deo Mishra Ohio State University Gilbert Cardwell H G M Automotive ElectronicsKrishnaswamy Srinivasan Ohio State University Citation: Mishra, K.D., Cardwell, G., and Srinivasan, K., “Modeling, Control, and Adaptation for Shift Quality Control of Automatic Transmissions,” SAE Technical Paper 2019-01-1129, 2019, doi:10.4271/2019-01-1129. Abstract The parameters determining shift quality control in automatic transmissions are determined as part of the calibration of the transmission control. The resulting control system typically has three components: feedforward control, where the control output is determined before a gear - shift; feedback control, where the control output is determined during the gearshift based on sensed feedback; and learning control (adaptation), where the feedforward or feedback controller parameters are modified after the current gearshift has ended and before the next similar gearshift begins. Gearshifts involving the same ratio change are referred to here as similar gearshifts, though such gearshifts may involve differences in other variables such as vehicle speed or engine torque. In most automatic transmissions, gearshifts are controlled by hydraulic clutches, and operating conditions for these clutches may vary widely, requiring a dedicated trans - mission controller involving significant calibration effort.In the current work, novel model-based methods are used to accomplish feedforward control of gearshifts, involving offline calibration of fill and torque phase control parameters and learning control of the fill phase. Towards this end, a physics-based model of the oncoming clutch involved in an upshift of a production automatic transmission was developed and experi - mentally validated against test bench experiments for a wide variety of inputs and operating conditions. The resulting model is used to generate a feedforward controller, offline model-based calibration algorithm, and a learning controller that corrects for clutch under-fill and over-fill. The effectiveness of the resulting controller is validated by simulation studies using the experi - mentally validated transmission hydraulic system model, in conjunction with a powertrain model. In particular, it is demon - strated that the learning controller corrects for initial under- or over-fill error in two to three gearshifts. Convergence and robustness properties, and transient performance of the learning controller are also discussed. Introduction A significant majority of production vehicles with auto-matic transmissions on the road today employ clutch-to-clutch shifts, which involve two actively controlled friction clutches capable of transmitting the load in both slip speed directions [1]; the clutch being released is known as the offgoing clutch, and the clutch being engaged is known as the oncoming clutch. For a typical gearshift, two fundamental specifications need to b