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

We propose a primal-dual interior-point (PDIP) method for solving quadraticprogramming problems with linear inequality constraints that typically ariseform MPC applications. We show that the solver converges (locally)quadratically to a suboptimal solution of the MPC problem. PDIP solvers rely ontwo phases: the damped and the pure Newton phases. Compared to state-of-the-artPDIP methods, our solver replaces the initial damped Newton phase (usually usedto compute a medium-accuracy solution) with a dual solver based on Nesterov'sfast gradient scheme (DFG) that converges with a sublinear convergence rate oforder O(1/k^2) to a medium-accuracy solution. The switching strategy to thepure Newton phase, compared to the state of the art, is computed in the dualspace to exploit the dual information provided by the DFG in the first phase.Removing the damped Newton phase has the additional advantage that our solversaves the computational effort required by backtracking line search. Theeffectiveness of the proposed solver is demonstrated on a 2-dimensionaldiscrete-time unstable system and on an aerospace application.

An Improved Primal-Dual Interior Point Solver for Model Predictive Control