Model Predictive Control for Automatic Transmission Upshift Inertia Phase

This article deals with model predictive control (MPC) design for automatic transmission (AT) upshift inertia phase, which aims to optimally coordinate the actions of oncoming (ONC) and off-going (OFG) clutches and engine and to facilitate calibration. The designed MPC strategy accounts for clutch actuation dynamics and constraints, while setting the tradeoff 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 ONC clutch slip speed tracking errors, and the shift efficiency is reflected in clutch energy loss minimization on a prediction horizon. This allows for the 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 online 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 one to three shift and its performance is compared with an offline, multiobjective 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.

Automated summary

This article presents the design of model predictive control (MPC) for the upshift inertia phase of an automatic transmission (AT), aiming to coordinate the actions of clutches, engine, and facilitate calibration. The designed MPC strategy considers clutch actuation dynamics and constraints, and balances three conflicting shift quality criteria: comfort, duration, and efficiency. The MPC problem is formulated as a constrained quadratic programming problem and solved efficiently online by an interior-point solver. The flexibility of the MPC design and its ease of calibration are demonstrated, and its performance is compared with an offline, multiobjective optimization-based control strategy.