Robust and two-level (nonlinear) predictive control of switched dynamical systems with unknown references for optimal wet-clutch engagement

Modeling and control of clutch engagement has been recognized as a challenging control problem, due to nonlinear and time-varying dynamics, that is, switching between two discontinuous dynamic phases: the fill and the slip. Furthermore, the reference trajectories for obtaining an optimal clutch engagement are not a priori known and may require adaptation to varying operating conditions. Two (nonlinear) model predictive control strategies are proposed based on the partial or full (non)linear identification of these two phases. First, a local linear model of the fill phase is identified and a robust model predictive control is designed to account for the consequent uncertainty in the slip phase. Second, (non)linear models of both the fill and the slip phases are identified and a two-level (nonlinear) model predictive control controller is proposed, where two (nonlinear) model predictive control controllers are designed for the two phases tracking references generated and continuously adapted by high-level iterative learning controllers. The robust and two-level (nonlinear) model predictive controls are validated on a real clutch. The results obtained from the real setup show that the proposed control strategies lead to an optimal engagement of the wet-clutch system.