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

Recently, a regional MPC approach has been proposed that exploits thepiecewise affine structure of the optimal solution (without computing theentire explicit solution before). Here, regional refers to the idea of usingthe affine feedback law that is optimal in a vicinity of the current state ofoperation, and therefore provides the optimal input signal without requiring tosolve a QP. In the present paper, we apply the idea of regional MPC to min-maxMPC problems. We show that the new robust approach can significantly reduce thenumber of QPs to be solved within min-max MPC resulting in a reduced overallcomputational effort. Moreover, we compare the performance of the new approachto an existing robust regional MPC approach using a numerical example withvarying horizon. Finally, we provide a rule for choosing a suitable robustregional MPC approach based on the horizon.

Reducing the computational effort of min-max model predictive control with regional feedback laws