Adaptive Koopman embedding for robust control of nonlinear dynamical systems
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R Singh, CK Sah, J Keshavan
The International Journal of …, 2024
journals.sagepub.com
The discovery of linear embedding is central to the synthesis of linear control techniques for nonlinear robotic systems. In recent years, data-driven Koopman operator-theoretic methods have been extensively used for learning these linear embeddings, although these algorithms often exhibit limitations in generalizability beyond the distribution captured by training data and are not robust to changes in the nominal system dynamics induced by intrinsic or environmental factors. To overcome these limitations, this study presents an …
