Adaptive Koopman-Based Models for Holistic Controller and Observer Design

Abstract

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.