000202643 001__ 202643
000202643 005__ 20180317092137.0
000202643 022__ $$a0143-2087
000202643 0247_ $$2doi$$a10.1002/oca.2140
000202643 037__ $$aARTICLE
000202643 245__ $$aDominant speed factors of active set methods for fast MPC
000202643 269__ $$a2014
000202643 260__ $$aHoboken$$bWiley-Blackwell$$c2014
000202643 336__ $$aJournal Articles
000202643 520__ $$aThe paper presents a review of active set (AS) algorithms that have been deployed for implementation of fast model predictive control (MPC). The main purpose of the survey is to identify the dominant features of the algorithms that contribute to fast execution of online MPC and to study their influence on the speed. The simulation study is conducted on two benchmark examples where the algorithms are analyzed in the number of iterations and in the workload per iteration. The obtained results suggest directions for potential improvement in the speed of existing AS algorithms.
000202643 6531_ $$amodel predictive control
000202643 6531_ $$aactive set methods
000202643 6531_ $$arank updates
000202643 6531_ $$acode generation
000202643 700__ $$aHerceg, M.$$uETH, Automat Control Lab, CH-8092 Zurich, Switzerland
000202643 700__ $$0246471$$aJones, Colin$$g207237
000202643 700__ $$aMorari, M.$$uETH, Automat Control Lab, CH-8092 Zurich, Switzerland
000202643 773__ $$j36$$k5$$q608-627$$tOptimal Control Applications & Methods
000202643 8564_ $$s847522$$uhttps://infoscience.epfl.ch/record/202643/files/HercegOCAM_2014.pdf$$yPublisher's version$$zPublisher's version
000202643 8564_ $$s388035$$uhttps://infoscience.epfl.ch/record/202643/files/fast_mpc_survey_ifaweb.pdf$$yPreprint$$zPreprint
000202643 909CO $$ooai:infoscience.tind.io:202643$$particle$$pSTI
000202643 909C0 $$0252490$$pLA3$$xU12397
000202643 917Z8 $$x207237
000202643 917Z8 $$x207237
000202643 937__ $$aEPFL-ARTICLE-202643
000202643 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000202643 980__ $$aARTICLE