This work addresses inverse dynamic games, which generalize the inverse problem of optimal control, and where the aim is to identify cost functions based on observed optimal trajectories. The identified cost functions can describe individual behavior in cooperative systems, e.g. human behavior in human-machine haptic shared control scenarios.

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 work focuses on the Limited Information Shared Control and its controller design using potential games. Through the developed systematic controller design, the experiments demonstrate the effectiveness and superiority of this concept compared to traditional manual and non-cooperative control approaches in the application of large vehicle manipulators.

The research reported in this thesis focuses on the decision making aspect of human-machine cooperation and reveals new insights from theoretical modeling to experimental evaluations: Two mathematical behavior models of two emancipated cooperation partners in a cooperative decision making process are introduced. The model-based automation designs are experimentally evaluated and thereby demonstrate their benefits compared to state-of-the-art approaches.

This work presents a real-time dynamic pricing framework for future electricity markets. Deduced by first-principles analysis of physical, economic, and communication constraints within the power system, the proposed feedback control mechanism ensures both closed-loop system stability and economic efficiency at any given time. The resulting price signals are able to incentivize competitive market participants to eliminate spatio-temporal shortages in power supply quickly and purposively.

This work introduces a new specification and verification approach for dynamic systems. The introduced approach is able to provide type II error free results by definition, i.e. there are no hidden faults in the verification result. The approach is based on Kaucher interval arithmetic to enclose the measurement in a bounded error sense. The developed methods are proven mathematically to provide a reliable verification for a wide class of safety critical systems.

Effective heat transport systems in aerospace are based on multiphase loop heat pipes (LHPs). For a precise thermal control of the electronics, electrical heaters are additionally used to control the operating temperature of the LHP. This work focusses on the dynamical modeling and model-based control design for LHP-based heat transport systems. The results of this work can be used for the optimization of current control parameters and the efficient control design for future LHP applications.

This work addresses the automated generation of physical-based models and model-based observers. We develop port-Hamiltonian methods, which for the first time allow a complete and consistent automation of these two processes for a large class of interconnected systems.

Reinforcement Learning is a promising tool to automate controller tuning. However, significant extensions are required for real-world applications to enable fast and robust learning. This work proposes several additions to the state of the art and proves their capability in a series of real world experiments.

Der Entwurf von Ansätzen zur marktbasierten Betriebsführung zukünftiger Energienetze steht vor der technischen Herausforderung, eine enorme Anzahl von Netzteilnehmern zeitlich und örtlich zu koordinieren, um Erzeugung und Verbrauch auszugleichen und einen sicheren Netzbetrieb zu ermöglichen. Um dieser Herausforderung zu begegnen entstand das Forschungsfeld der Transactive Control Ansätze. In dieser Arbeit wird ein neuer Transactive Control Ansatz für gekoppelte Strom- und Wärmenetze vorgestellt. - The design of approaches for future market-based energy network operation faces the technical challenge of needing to coordinate a vast number of network participants spatially and temporally, in order to balance energy supply and demand, while achieving secure network operation. To meet this challenge, the research field of transactive control emerged. Within this work a new transactive control approach for coupled electric power and district heating networks is presented.