In this research, a new hybrid neural network is developed to model engagement behaviors of automotive transmission wet friction component. Utilizing known first principles on the physics of engagement, special modules are created to estimate viscous torque and asperity contact torque as preprocessors to a two-layer neural network. Inside these modules, all the physical parameters are represented by neurons with various activation functions derived from first principles. These new features contribute to the improved performance and trainability over a conventional two-layer network model. Both the hybrid and conventional neural net models are trained and tested with experimental data collected from an SAE#2 test stand. The results show that the performance of the hybrid model is much superior to that of the conventional model. It successfully captures detailed characteristics of the friction component engagement torque as a function of time over a wide operating range.
A Hybrid Neural Network Approach for the Development of Friction Component Dynamic Model
Contributed by the Dynamic Systems, Measurement, and Control Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received by the ASME Dynamic Systems and Control Division May 14, 2002; final revision, August 25, 2003; Associate Editor: R. Langari.
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Cao , M., Wang, K. W., Fujii , Y., and Tobler, W. E. (April 12, 2004). "A Hybrid Neural Network Approach for the Development of Friction Component Dynamic Model ." ASME. J. Dyn. Sys., Meas., Control. March 2004; 126(1): 144–153. https://doi.org/10.1115/1.1649980
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