Comparison of two-level NMPC and ILC strategies for wet-clutch control

Abstract Modeling and control of clutch engagements has been recognized as a challenging problem, due to nonlinear and time-varying dynamics, switching discontinuously between two phases. Furthermore, the optimal references are not known a priori and vary with operating conditions. To address these issues a two-level control scheme is proposed, consisting of a learning algorithm at the high level, updating parameterized references to be tracked at the low level. To simplify the tracking, the controls for both phases are separated. In a first implementation, two (non)linear model predictive controllers (NMPCs) are used sequentially, while in a second implementation these are replaced by two Iterative Learning Controllers (ILCs). The performance and robustness are investigated on a test setup with wet-clutches, and it is shown that both implementations combined with suitable high level algorithms result in good engagements.

[1]  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.

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

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

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

[5]  Richard W. Longman,et al.  Iterative learning control and repetitive control for engineering practice , 2000 .

[6]  David Q. Mayne,et al.  Model predictive control: Recent developments and future promise , 2014, Autom..

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

[8]  Xuping Xu,et al.  Optimal control of switched systems: new results and open problems , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[9]  M. Grotjahn,et al.  Modelling and identification of car driveline dynamics for anti-jerk controller design , 2006, 2006 IEEE International Conference on Mechatronics.

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

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

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

[13]  Jo Yung Wong,et al.  Theory of ground vehicles , 1978 .

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

[15]  Abhishek Dutta,et al.  Switched Nonlinear Predictive Control with Adaptive References for Engagement of Wet Clutches , 2012 .

[16]  Liuping Wang,et al.  Model Predictive Control System Design and Implementation Using MATLAB , 2009 .

[17]  Ilya V. Kolmanovsky,et al.  Predictive energy management of a power-split hybrid electric vehicle , 2009, 2009 American Control Conference.

[18]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

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

[20]  Abhishek Dutta,et al.  Robust predictive control design for optimal wet-clutch engagement , 2012, 2012 American Control Conference (ACC).

[21]  A.G. Alleyne,et al.  A survey of iterative learning control , 2006, IEEE Control Systems.

[22]  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).