I. Cvok, J. Soldo, J. Deur, V. Ivanovic, Y. Zhang, Y. Fujii

Model Predictive Control for Automatic Transmission Upshift Inertia Phase

IEEE Transactions on Control Systems Technology, Vol. 31, No. 6, pp. 2335-2349, 2023
The paper deals with model predictive control (MPC) design for automatic transmission (AT) upshift inertia phase, which aims to optimally coordinate the actions of oncoming and off-going clutches and engine, and to facilitate calibration. The designed MPC strategy accounts for clutch actuation dynamics and constraints, while setting the trade-off between three key and conflicting shift quality criteria: comfort, duration, and efficiency. The shift comfort and duration are ensured by minimizing output shaft torque and oncoming clutch slip speed tracking errors, and the shift efficiency is reflected in clutch energy loss minimization on a prediction horizon. This allows for calibration of the MPC performance through setting the inertia phase duration, the output shaft torque reference, cost function weighting coefficients, and constraints, rather than optimizing the shift control profiles themselves. The MPC problem is formulated as a constrained quadratic programming problem and efficiently solved on-line by an interior-point solver. The proposed MPC strategy is applicable to other transmissions with multiple actuators, such as parallel hybrid transmissions. The MPC system is examined through nonlinear powertrain model simulations for 1-3 shift and its performance is compared with an off-line, multi-objective optimization-based control strategy. The MPC design flexibility and ease of calibration are demonstrated for different shift comfort and duration targets, as well as cost function tuning, and robustness with respect to clutch actuation parameter uncertainties is examined.