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

In this work, we consider the task of improving the accuracy of dynamicmodels for model predictive control (MPC) in an online setting. Althoughprediction models can be learned and applied to model-based controllers, thesemodels are often learned offline. In this offline setting, training data isfirst collected and a prediction model is learned through an elaboratedtraining procedure. However, since the model is learned offline, it does notadapt to disturbances or model errors observed during deployment. To improvethe adaptiveness of the model and the controller, we propose an online dynamicslearning framework that continually improves the accuracy of the dynamic modelduring deployment. We adopt knowledge-based neural ordinary differentialequations (KNODE) as the dynamic models, and use techniques inspired bytransfer learning to continually improve the model accuracy. We demonstrate theefficacy of our framework with a quadrotor, and verify the framework in bothsimulations and physical experiments. Results show that our approach canaccount for disturbances that are possibly time-varying, while maintaining goodtrajectory tracking performance.

Online Dynamics Learning for Predictive Control with an Application to Aerial Robots