Self-learning approach to automatic transmission shift control in a commercial construction vehicle during the inertia phase

Abstract

Electrohydraulic shift control of a vehicle automatic transmission has been predominantly carried out via open-loop control based on numerous time-consuming calibrations. Despite remarkable success in practice, the variations of system characteristics inevitably cause the performance of the tuned open-loop controller to deteriorate. As a result, the controller parameters need to be continuously updated in order to maintain satisfactory shift quality. This paper presents a selflearning algorithm for automatic transmission shift control in a commercial construction vehicle during the inertia phase. First, an observer reconstructs the turbine acceleration signal (impossible to measure in a commercial construction vehicle) from the readily accessible turbine speed measurement. Then, a control algorithm based on a quadratic cost function of the turbine acceleration is shown to guarantee the asymptotic convergence (within a specified target bound) of the error between the actual and the desired turbine accelerations. A Lyapunov argument plays a crucial role in deriving adaptive laws for control parameters. The simulation and hardware-in-the-loop simulation studies show that the proposed algorithm actually delivers the promise of satisfactory performance despite the variations and uncertainties of system characteristics.