论文标题
基于自适应库普曼的整体控制器和观察者设计的模型
Adaptive Koopman-Based Models for Holistic Controller and Observer Design
论文作者
论文摘要
我们提出了一种获得基于数据驱动的Koopman操作员模型的方法，该模型在操作过程中适应自身，并且可以直接用于控制器和观察者设计。自适应模型能够准确地描述不同的状态空间区域，并考虑操作过程中发生的不可预测的系统变化。此外，我们表明这种自适应模型适用于状态空间控制，这需要对状态向量的完全了解。因此，为了改变系统动力学，国家观察者还需要具有适应能力。据我们所知，目前尚无所有方法可以全面地使用基于Koopman的植物模型来进行控制器和观察者的状态空间设计。我们在测试钻机上演示了我们的方法：控制器和观察者在操作过程中充分适应，因此即使在发生强大的发生系统变化的情况下，也可以实现出色的控制性能。
We present a method to obtain a data-driven Koopman operator-based model that adapts itself during operation and can be straightforwardly used for the controller and observer design. The adaptive model is able to accurately describe different state-space regions and additionally consider unpredictable system changes that occur during operation. Furthermore, we show that this adaptive model is applicable to state-space control, which requires complete knowledge of the state vector. For changing system dynamics, the state observer therefore also needs to have the ability to adapt. To the best of our knowledge, there have been no approaches presently available that holistically use an adaptive Koopman-based plant model for the state-space design of both the controller and observer. We demonstrate our method on a test rig: controller and observer adequately adapt during operation, so that outstanding control performance is achieved even in the case of strong occuring systems changes.
