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{ "item_title" : "Model Predictive Control", "item_author" : [" Aris Daniilidis", "Lars Grüne", "Josef Haunschmied "], "item_description" : "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. ", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/03/185/255/3031852559_b.jpg", "price_data" : { "retail_price" : "199.99", "online_price" : "199.99", "our_price" : "199.99", "club_price" : "199.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Model Predictive Control|Aris Daniilidis

Model Predictive Control : Engineering Methods for Economists

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Overview

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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Details

  • ISBN-13: 9783031852558
  • ISBN-10: 3031852559
  • Publisher: Springer
  • Publish Date: June 2025
  • Dimensions: 9.21 x 6.14 x 0.56 inches
  • Shipping Weight: 1.13 pounds
  • Page Count: 224

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