Model Predictive Control Engineering Methods for Economists /

Dettagli Bibliografici
Ente Autore:	SpringerLink (Online service)
Altri autori:	Daniilidis, Aris (Redattore), Grüne, Lars (Redattore), Haunschmied, Josef (Redattore), Tragler, Gernot (Redattore)
Riassunto:	XII, 224 p. 148 illus., 124 illus. in color. text
Lingua:	inglese
Pubblicazione:	Cham : Springer Nature Switzerland : Imprint: Springer, 2025.
Edizione:	1st ed. 2025.
Serie:	Dynamic Modeling and Econometrics in Economics and Finance, 31
Soggetti:	Econometrics. Operations research. Social sciences > Mathematics. Stochastic processes. Control engineering. Quantitative Economics. Operations Research and Decision Theory. Mathematics in Business, Economics and Finance. Stochastic Systems and Control. Control and Systems Theory.
Accesso online:	https://doi.org/10.1007/978-3-031-85256-5
Natura:	Elettronico Libro

Sommario:

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).

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