000169724 001__ 169724
000169724 005__ 20190316235223.0
000169724 037__ $$aCONF
000169724 245__ $$aInput-to-state stabilization of feasible model predictive controllers for linear systems
000169724 269__ $$a2011
000169724 260__ $$c2011
000169724 336__ $$aConference Papers
000169724 520__ $$aResearch on sub-optimal Model Predictive Control (MPC) has led to a variety of optimization methods based on explicit or online approaches, or combinations thereof. Its foremost aim is to guarantee essential controller properties, i.e. recursive feasibility, stability, and robustness, at reduced and predictable computational cost, i.e. computation time and storage space. This paper shows how the input sequence of any (not necessarily stabilizing) sub-optimal controller and the shifted input sequence from the previous time step can be used in an optimal convex combination, which is easy to determine online, in order to guarantee input-to-state stability for the closed-loop system. The presented method is thus able to stabilize a wide range of existing sub-optimal MPC schemes that lack a formal stability guarantee, if they can be considered as a continuous map from the state space to the space of feasible input sequences.
000169724 700__ $$aSchildbach, Georg
000169724 700__ $$0(EPFLAUTH)214806$$g214806$$aZeilinger, Melanie Nicole
000169724 700__ $$aMorari, Manfred
000169724 700__ $$0246471$$g207237$$aJones, Colin
000169724 7112_ $$dDecember, 2011$$cOrlando, Florida$$aIEEE Conference on Decision & Control
000169724 773__ $$tProceedings of the IEEE Conference on Decision & Control
000169724 8564_ $$uhttps://infoscience.epfl.ch/record/169724/files/FeasibleISS_v11.pdf$$zPreprint$$s291317$$yPreprint
000169724 909C0 $$0252053$$pLA
000169724 909CO $$pSTI$$ooai:infoscience.tind.io:169724$$qGLOBAL_SET$$pconf
000169724 917Z8 $$x207237
000169724 937__ $$aEPFL-CONF-169724
000169724 973__ $$rREVIEWED$$sACCEPTED$$aEPFL
000169724 980__ $$aCONF