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Obtaining a Stabilizing Prediction Horizon in Quadratic Programming Model Predictive Control

 
: Morgenstern, Dimitri; Görges, Daniel; Wirsen, Andreas

:

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 58th Conference on Decision and Control, CDC 2019 : Nice, France, December 11-13, 2019
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-1398-2
ISBN: 978-1-7281-1397-5
ISBN: 978-1-7281-1399-9
S.463-467
Conference on Decision and Control (CDC) <58, 2019, Nice>
Englisch
Konferenzbeitrag
Fraunhofer ITWM ()

Abstract
In this paper, it is shown how a performance tuple can be obtained in model predictive control if the optimal control problem is a quadratic program. The quotient of the finite-horizon optimal cost and the tuple's first entry upper bounds the sum of all instances over the finite-horizon optimal cost. The tuple's second entry is a stabilizing prediction horizon. The algorithm taking the describing matrices and giving a performance tuple is easily verifiable.

: http://publica.fraunhofer.de/dokumente/N-585569.html