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

This paper presents a discrete-time nonlinear system identification methodwhile satisfying the stability and safety properties of the system with highprobability. An Extreme Learning Machine (ELM) is used with a Gaussianassumption on the function reconstruction error. A quadratically constrainedquadratic program (QCQP) is developed with probabilistic safety and stabilityconstraints that are only required to be satisfied at sampled points inside theinvariant region. The proposed method is validated using two simulationexamples: a two degrees-of-freedom (DoF) robot manipulator with constraints onjoint angles whose trajectories are guaranteed to remain inside a safe set andon motion trajectories data of a hand-drawn shape.

Discrete-Time Nonlinear Systems Identification with Probabilistic Safety and Stability Constraints