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In this work, we present a novel approach that combines the power of Koopman operators and deep neural networks to generate a linear representation of the Duffing oscillator. This approach enables effective parameter estimation and accurate prediction of the oscillator's future behavior.
This repository contains the code for bilinear models for serial manipulators and the corresponding ZNN controller developed for the purpose of trajectory tracking with the aforementioned models.
This repository is a supplementary documentation for the Multi-Model Parameterized Koopman (MMPK) framework capturing results through software and hardware deployments