Switched Nonlinear Predictive Control with Adaptive References for Engagement of Wet Clutches

This paper discusses the control of nonlinear systems where the reference trajectories are not known beforehand, e.g. the engagement of a wet clutch. The wet clutch is considered as a combination of two disjoint dynamic phases of linear fill and nonlinear slip. Subsequently, a (non)linear system identification is performed of these two phases. With these models at hand, we design separate control strategies. In this paper we show the added value of using a nonlinear model predictive controller (NMPC) to control the nonlinear slip, while a linear MPC suffices for the fill phase control. The reference trajectories for these two phases and the switching moment between them are learnt iteratively. Our results show that nonlinear MPC outperforms linear MPC for the slip phase, leading to an optimal engagement of a real wet clutch setup.