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 systematic approach to nonlinear state-feedback controldesign that has three main advantages: (i) it ensures exponential stability and$ \mathcal{L}_2 $-gain performance with respect to a user-defined set ofreference trajectories, and (ii) it provides constructive conditions based onconvex optimization and a path-integral-based control realization, and (iii) itis less restrictive than previous similar approaches. In the proposed approach,first a virtual representation of the nonlinear dynamics is constructed forwhich a behavioral (parameter-varying) embedding is generated. Then, byintroducing a virtual control contraction metric, a convex control synthesisformulation is derived. Finally, a control realization with a virtual referencegenerator is computed, which is guaranteed to achieve exponential stability and$ \mathcal{L}_2 $-gain performance for all trajectories of the targetedreference behavior. We show that the proposed methodology is a unifiedgeneralization of the two distinct categories of linear-parameter-varying (LPV)state-feedback control approaches: global and local methods. Moreover, itprovides rigorous stability and performance guarantees as a method fornonlinear tracking control, while such properties are not guaranteed fortracking control using standard LPV approaches.

Virtual Control Contraction Metrics: Convex Nonlinear Feedback Design via Behavioral Embedding