1.

Record Nr.

UNINA9911009338003321

Autore

Daniilidis Aris

Titolo

Model Predictive Control : Engineering Methods for Economists / / edited by Aris Daniilidis, Lars Grüne, Josef Haunschmied, Gernot Tragler

Pubbl/distr/stampa

Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025

ISBN

3-031-85256-7

Edizione

[1st ed. 2025.]

Descrizione fisica

1 online resource (288 pages)

Collana

Dynamic Modeling and Econometrics in Economics and Finance, , 2363-8370 ; ; 31

Altri autori (Persone)

GrüneLars

HaunschmiedJosef

TraglerGernot

Disciplina

629.8

Soggetti

Econometrics

Operations research

Social sciences - Mathematics

Stochastic processes

Automatic control

Quantitative Economics

Operations Research and Decision Theory

Mathematics in Business, Economics and Finance

Stochastic Systems and Control

Control and Systems Theory

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

Chapter 1. Multi-horizon MPC and Its Application to theIntegrated Power and Thermal Management ofElectrified Vehicles (Qiuhao Hu) -- Chapter 2. Data/Moment-Driven Approaches for FastPredictive Control of Collective Dynamics (Giacomo Albi) -- Chapter 3. Finite-Dimensional Receding Horizon Control ofLinear Time-Varying Parabolic PDEs: StabilityAnalysis and Model-Order Reduction (Behzad Azmi) -- Chapter 4. Solving Hybrid Model Predictive ControlProblems via a Mixed-Integer Approach (Iman Nodozi) -- Chapter 5. nMPyC – A Python Package for Solving OptimalControl Problems via Model Predictive Control (Jonas Schießl) -- Chapter 6. Controllability of Continuous Networks and



aKernel-Based Learning Approximation (Michael Herty) -- Chapter 7. Economic Model Predictive Control as aSolution to Markov Decision Processes (Dirk Reinhardt) -- Chapter 8. Reinforcement Learning with Guarantees (Mario Zanon).

Sommario/riassunto

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.