The primary goal of this research was to identify the role of non-linear parameter estimation in clutch torque estimation for automotive applications. The benefit of this approach in estimating parameters of a system defined by a set of differential algebraic equations (DAEs) that represents, say, clutch torque profile is investigated. In addition, online implementation, albeit at a slow rate compared to control loop rates is demonstrated. This method of analytical DAE based estimation has significant advantages over purely empirical methods in that it directly identifies the relationships between system problem variables, such as, engine speed, cross shaft angle to variable of interest, say, clutch torque, unlike, indirect approaches [1]. In addition, in contrast with time series evolution of discrete system models based on ARMAX models, this approach allows a designer to know the relationship between system parameters such as friction coefficient, clutch engagement angle, etc and the estimation process leading to better design of clutch control algorithms. However, unlike, the direct digital control methods, DAE based approaches are computationally more intensive resulting in a need for additional onboard processing. One of the goals of this initial research is to study this to identify practical analytical and numerical approaches that will lead to onboard implementation of these algorithms for truck applications, specifically in automated mechanical transmissions (AMTs) [2, 3].

1.
Goodwin, G. C., and Sin, K. S., Adaptive Filtering.Prediction and Control, Prentice Hall, Engelwood Cliffs, 1989.
2.
Garofalo, F., et al. “Optimal Tracking for Automotive Dry Clutch Engagement,” Proceedings of the 15th Triennial World Congress, Barcelona, Spain.
3.
Bemporad, A., F. Borrelli, L. Glielmo and F. Vasca (2001). Hybrid control of dry clutch engagement. In: Proc. of European Control Conference.
4.
Levine, J., and Remond, B., “Flatness Based Control of an Automatic Clutch.”
5.
Garofalo, F., L. Glielmo, L. Iannelli and F. Vasca (2001). Smooth engagement for automotive dry clutch. In: Proc. of the 40th IEEE Conference on Decision and Control. IEEE.
6.
Joshi, S., Liu, J., and Ananthakrishnan S., A System for Clutch Torque Management, Invention Disclosure filed with the United States Patent Office.
7.
Schittkowski, K., Numerical Data Fitting in Dynamical Systems — A Practical Introduction with Applications and Software, Kluwer Academic Publishers, 2002.
8.
http://www.mathworks.com/company/user_stories/userstor y2703.htm.
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