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Model Predictive Control : Engineering Methods for Economists

Title data

Model Predictive Control : Engineering Methods for Economists.
ed.: Daniilidis, Aris ; Grüne, Lars ; Haunschmied, Josef ; Tragler, Gernot
Cham, Switzerland : Springer , 2025 . - 198 p. - (Dynamic Modeling and Econometrics in Economics and Finance ; 31 )
ISBN 978-3-031-85255-8

Official URL: Volltext

Abstract in another language

The book explores the field of model predictive control (MPC). It reports on the latest developments in MPC, current applications, and presents various subfields of MPC. The book features topics such as uncertain and stochastic MPC variants, learning and neural network approaches, easy-to-use numerical implementations as well as multi-agent systems and scheduling and coordination tasks. While MPC is rooted in engineering science, this book illustrates the potential of using MPC theory and methods in non-engineering sciences and applications such as economics, finance, and environmental sciences.

Further data

Item Type: Book / Monograph
Keywords: Economic Dynamics; Control Systems; Dynamical models; Stochastic methods in Control; Stabilization and Optimal Control; Model Predictive Control; Control and Systems Theory; Nonlinear Systems; Markov Decision Problems
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics V (Applied Mathematics)
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics V (Applied Mathematics) > Chair Mathematics V (Applied Mathematics) - Univ.-Prof. Dr. Lars Grüne
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Applied Mathematics > Chair Applied Mathematics - Univ.-Prof. Dr. Anton Schiela
Profile Fields > Advanced Fields > Nonlinear Dynamics
Research Institutions > Central research institutes > Bayreuth Research Center for Modeling and Simulation - MODUS
Result of work at the UBT: Yes
DDC Subjects: 300 Social sciences > 330 Economics
500 Science > 510 Mathematics
Date Deposited: 13 Feb 2025 07:19
Last Modified: 13 Feb 2025 07:19
URI: https://eref.uni-bayreuth.de/id/eprint/92353