English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Tracking performance of physical-model-based feedforward control forinterventional X-ray systems is limited by hard-to-model parasitic nonlineardynamics, such as cable forces and nonlinear friction. In this paper, thesenonlinear dynamics are compensated using a physics-guided neural network(PGNN), consisting of a physical model, embedding prior knowledge of thedynamics, in parallel with a neural network to learn hard-to-model dynamics. Toensure that the neural network learns only unmodelled effects, the neuralnetwork output in the subspace spanned by the physical model is regularized viaan orthogonal projection-based approach, resulting in complementary physicalmodel and neural network contributions. The PGNN feedforward controller reducesthe tracking error of an interventional X-ray system by a factor of 5 comparedto an optimally tuned physical model, successfully compensating the unmodeledparasitic dynamics.

Learning for Precision Motion of an Interventional X-ray System: Add-on Physics-Guided Neural Network Feedforward Control