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Model-based and model-free learning strategies for wet clutch control
| Content Provider | Semantic Scholar |
|---|---|
| Author | Dutta, Abhishek Zhong, Yu Depraetere, Bruno Vaerenbergh, Kevin Van Ionescu, C. Wyns, Bart Pinte, Gregory Nowé, Ann Swevers, Jan Keyser, Robain De |
| Copyright Year | 2014 |
| Abstract | This paper presents an overview of model-based (Nonlinear Model Predictive Control, Iterative Learning Control and Iterative Optimization) and model-free (Genetic-based Machine Learning and Reinforcement Learning) learning strategies for the control of wet-clutches. The benefits and drawbacks of the different methodologies are discussed, and illustrated by an experimental validation on a test bench containing wet-clutches. In general, all strategies yield a good engagement quality once they converge. The model-based strategies seems most suited for an online application, because they are inherently more robust and require a shorter convergence time. The model-free strategies meanwhile seem most suited to offline calibration procedures for complex systems where heuristic tuning rules no longer suffice. |
| Starting Page | 1008 |
| Ending Page | 1020 |
| Page Count | 13 |
| File Format | PDF HTM / HTML |
| DOI | 10.1016/j.mechatronics.2014.03.006 |
| Volume Number | 24 |
| Alternate Webpage(s) | https://biblio.ugent.be/publication/5863463/file/5863485.pdf |
| Alternate Webpage(s) | https://doi.org/10.1016/j.mechatronics.2014.03.006 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
