Nonlinear model-predictive control with hysteresis compensation of an electro-pneumatic clutch for truck applications

Abstract

This contribution presents two real-time capable nonlinear model-predictive control (NMPC) approaches for an electro-pneumatic clutch for heavy trucks: a centralized control approach and a cascaded one. A clutch is necessary at start-up or during gear shifts to connect or disconnect the combustion engine and the gear box. This automated actuator disburdens the driver and provides the necessary actuation force according to the large torque typically transmitted through the drive train. The force characteristic of the clutch, however, is subject to hysteresis, which is described by a generalized Bouc–Wen model and used for a feedforward hysteresis compensation in the control algorithm. The proposed NMPC-algorithm involves (i) a minimization of the difference between the desired and predicted state vector at the end of the prediction horizon and (ii) flatness-based techniques to compute desired trajectories for the complete state vector as well as the control input. The optimal control is given by an additional, minimum-norm control input that minimizes the difference between the predicted state vector and the desired one at the end of the prediction horizon. Thereby, the computation effort of the NMPC approaches can be kept relatively small, and a real-time evaluation becomes possible. A reduced-order observer estimates an effective pressure in the clutch that also accounts for an uncertain disturbance force. Thereby, a disturbance compensation and a high tracking accuracy is achievable for the piston position as controlled variable. The efficiency of the two proposed control structures is emphasized by experimental results from a dedicated test rig.