Switched Predictive Control Design for Optimal Wet-clutch Engagement

Identification and control of wet-clutches has been recognized as a challenging control problem, due to their non-linear time-varying dynamics and their discontinuous switching between filling and slipping phases. In this paper, our approach is based on a full (non)linear system identification of the filling and slipping phases, followed by two closed loop model predictive controllers (MPC) for the two phases and switching between these controllers at suitable moments for optimal torque transfer. Further, it is shown that an intelligent formulation of the switching function is beneficial to the robustness against time-varying model parameters. Finally, our control technique is validated on a real-life wet-clutch test-bench to obtain optimal engagement.