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Stability and performance guarantees for model predictive control algorithms without terminal constraints
Author(s) -
Pannek J.,
Worthmann K.
Publication year - 2014
Publication title -
zamm ‐ journal of applied mathematics and mechanics / zeitschrift für angewandte mathematik und mechanik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.449
H-Index - 51
eISSN - 1521-4001
pISSN - 0044-2267
DOI - 10.1002/zamm.201100133
Subject(s) - model predictive control , bottleneck , controllability , horizon , stability (learning theory) , control theory (sociology) , computer science , mathematical optimization , mathematics , algorithm , control (management) , artificial intelligence , machine learning , embedded system , geometry
Abstract A typical bottleneck of model predictive control algorithms is the computational burden in order to compute the receding horizon feedback law which is predominantly determined by the length of the prediction horizon. Based on a relaxed Lyapunov inequality we present techniques which allow us to show stability and suboptimality estimates for a reduced prediction horizon. In particular, the known structural properties of suboptimality estimates based on a controllability condition are used to cut the gap between theoretic stability results and numerical observations.