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.

[1]  Jay H. Lee,et al.  Model predictive control: Review of the three decades of development , 2011 .

[2]  G. Pinte,et al.  Learning strategies for wet clutch control , 2011, 15th International Conference on System Theory, Control and Computing.

[3]  Zongxuan Sun,et al.  Automotive transmission clutch fill optimal control: An experimental investigation , 2010, Proceedings of the 2010 American Control Conference.

[4]  Robain De Keyser.  Model based predictive control , 1991 .

[5]  Jan Swevers,et al.  Iterative learning control for the filling of wet clutches , 2010 .

[6]  M. Kanat Camlibel,et al.  Hybrid optimal control of dry clutch engagement , 2007, Int. J. Control.

[7]  R. De Keyser,et al.  Robust nonlinear extended prediction self-adaptive control (NEPSAC) of continuous bioreactors , 2012, 2012 20th Mediterranean Conference on Control & Automation (MED).

[8]  Roberto Zanasi,et al.  Modeling and Control of Wet Clutches by Pressure-Control Valves , 2004 .

[9]  Zongxuan Sun,et al.  Challenges and opportunities in automotive transmission control , 2005, Proceedings of the 2005, American Control Conference, 2005..

[10]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[11]  Jan Swevers,et al.  A two-level Iterative Learning Control scheme for the engagement of wet clutches , 2011 .

[12]  Johan Schoukens,et al.  Nonlinear system-identification of the filling phase of a wet-clutch system , 2011 .

[13]  Vu Trieu Minh,et al.  Development of a real-time clutch transition strategy for a parallel hybrid electric vehicle , 2012, J. Syst. Control. Eng..

[14]  L. Glielmo,et al.  Engagement control for automotive dry clutch , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[15]  Abhishek Dutta,et al.  Robust predictive control of a wet-clutch using evolutionary algorithm optimized engagement profile , 2011, 15th International Conference on System Theory, Control and Computing.

[16]  David W. Clarke,et al.  Generalized predictive control - Part I. The basic algorithm , 1987, Autom..