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Model predictive controlengineering methods for economists /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Model predictive controledited by Aris Daniilidis ... [et al.].
Reminder of title:	engineering methods for economists /
other author:	Daniilidis, Aris.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xii, 224 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Predictive control.
Online resource:	https://doi.org/10.1007/978-3-031-85256-5
ISBN:	9783031852565$q(electronic bk.)

Model predictive controlengineering methods for economists /
Model predictive controlengineering methods for economists /[electronic resource] :edited by Aris Daniilidis ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xii, 224 p. :ill. (some col.), digital ;24 cm. - Dynamic modeling and econometrics in economics and finance,v. 312363-8370 ;. - Dynamic modeling and econometrics in economics and finance ;v.17..

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).

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.

ISBN: 9783031852565$q(electronic bk.)

Standard No.: 10.1007/978-3-031-85256-5doiSubjects--Topical Terms:

182316
Predictive control.

LC Class. No.: TJ217.6

Dewey Class. No.: 629.8

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505 0 $a 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).
520 $a 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.
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