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Iterative learning control for the filling of wet clutches

机译：填充湿式离合器的迭代学习控制

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This paper discusses the development of an advanced iterative learning control (ILC) scheme for the filling of wet clutches. In the presented scheme, the appropriate actuator signal for a new clutch engagement is learned automatically based on the quality of previous engagements, such that time-consuming and cumbersome calibrations can be avoided. First, an 1LC controller, which uses the position of the piston as control input, is developed and tested on a non-rotating clutch under well controlled conditions. Afterwards, a similar strategy is tested on a rotating set-up, where a pressure sensor is used as the input of the ILC controller. On a higher level, both the position and the pressure controller are extended with a second learning algorithm, that adapts the reference position/pressure to account for environmental changes which cannot be learned by the low-level ILC controller. It is shown that a strong reduction of the transmitted torque level as well as a significant shortening of the engagement time can be achieved with the developed strategy, compared to traditional time-invariant control strategies.
机译：本文讨论了用于填充湿式离合器的高级迭代学习控制（ILC）方案的开发。在提出的方案中，基于先前接合的质量自动获知用于新离合器接合的合适的致动器信号，从而可以避免耗时且麻烦的校准。首先，开发了一个1LC控制器，该控制器将活塞的位置用作控制输入，并在控制良好的条件下在非旋转离合器上进行了测试。此后，在旋转装置上测试了类似的策略，其中压力传感器用作ILC控制器的输入。在更高的级别上，位置和压力​​控制器都扩展了第二种学习算法，该算法可调整参考位置/压力以解决低级ILC控制器无法学习的环境变化。结果表明，与传统的时不变控制策略相比，通过所开发的策略可以大大降低传递的扭矩水平，并显着缩短接合时间。

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