Comparison of two-level NMPC and ILC strategies for wet-clutch control
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- 作者：Abhishek Dutta ; Bruno Depraetere ; Clara Ionescu ; Gregory Pinte ; Jan Swevers ; Robin De Keyser
- 关键词：Nonlinear ; Predictive ; Iterative ; Learning ; Mechatronics ; Clutch
- 刊名：Control Engineering Practice
- 出版年：January, 2014
- 年：2014
- 卷：22
- 期：Complete
- 页码：114-124
- 全文大小：1313 K
文摘
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.