Inverse Optimal Control and Inverse Noncooperative Dynamic Game Theory
A Minimum-Principle Approach
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Beschreibung
This book presents a novel unified treatment of inverse problems in optimal control and noncooperative dynamic game theory. It provides readers with fundamental tools for the development of practical algorithms to solve inverse problems in control, robotics, biology, and economics. The treatment involves the application of Pontryagin's minimum principle to a variety of inverse problems and proposes algorithms founded on the elegance of dynamic optimization theory. There is a balanced emphasis between fundamental theoretical questions and practical matters.
The text begins by providing an introduction and background to its topics. It then discusses discrete-time and continuous-time inverse optimal control. The focus moves on to differential and dynamic games and the book is completed by consideration of relevant applications.
The algorithms and theoretical results developed in Inverse Optimal Control and Inverse Noncooperative Dynamic Game Theory provide new insights into information requirements for solving inverse problems, including the structure, quantity, and types of state and control data. These insights have significant practical consequences in the design of technologies seeking to exploit inverse techniques such as collaborative robots, driver-assistance technologies, and autonomous systems. The book will therefore be of interest to researchers, engineers, and postgraduate students in several disciplines within the area of control and robotics.
This book presents a novel unified treatment of inverse problems in optimal control and noncooperative dynamic game theory. It provides readers with fundamental tools for the development of practical algorithms to solve inverse problems in control, robotics, biology, and economics. The treatment involves the application of Pontryagin's minimum principle to a variety of inverse problems and proposes algorithms founded on the elegance of dynamic optimization theory. There is a balanced emphasis between fundamental theoretical questions and practical matters.
The text begins by providing an introduction and background to its topics. It then discusses discrete-time and continuous-time inverse optimal control. The focus moves on to differential and dynamic games and the book is completed by consideration of relevant applications.
The algorithms and theoretical results developed in Inverse Optimal Control and Inverse Noncooperative Dynamic Game Theory provide new insights into information requirements for solving inverse problems, including the structure, quantity, and types of state and control data. These insights have significant practical consequences in the design of technologies seeking to exploit inverse techniques such as collaborative robots, driver-assistance technologies, and autonomous systems. The book will therefore be of interest to researchers, engineers, and postgraduate students in several disciplines within the area of control and robotics.
Presents a unified discussion of inverse problems in optimal control and noncooperative dynamic game theory Equips readers with insights to pose and analyse inverse problems in control, robotics, economics and machine learning Provides a taste of practical applications using case studies and examples
Autor*in
Timothy L. Molloy
Themen in »Inverse Optimal Control and Inverse Noncooperative Dynamic Game Theory«
Inverse Optimal Control Inverse Dynamic Game Theory Inverse Dynamic Games Inverse Differential Games Minimum Principle Optimal Control Game Theory Riccati Equations
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“Inverse Optimal Control and Inverse Noncooperative Dynamic Game Theory is highly recommended for advanced students, researchers, and professionals in control theory, applied mathematics, and game theory who aim to delve into inverse problems in multiagent settings. ... With its blend of theory, examples, and case studies, it offers not only rigorous mathematical insights, but also a clear path to applying these concepts in real-world scenarios. This makes it an essential resource for the next generation of researchers … .” (Sebastián Zamorano Aliaga, SIAM Review, Vol. 67 (3), September, 2025)
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