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With respect to the future urban mobility, modern electrical bicycles, advanced motorcycles and innovative two-wheeled vehicles are arresting enormous amount of attention. Especially, model-based control and optimal trajectory planning for such vehicles are important to the research and development of the future. Therefore, a reliable and yet usable vehicle model as well as a systematic approach to motion control for two-wheeled vehicles are essential, to which this work makes a contribution. Currently available two-wheeled vehicle models are mostly either too complex to be used for a systematic control synthesis, or too simple such that the physical behaviour of the vehicle is no more repre...
In recent years, researchers have achieved great success in guaranteeing safety in human-robot interaction, yielding a new generation of robots that can work with humans in close proximity, known as collaborative robots (cobots). However, due to the lack of ability to understand and coordinate with their human partners, the ``co'' in most cobots still refers to ``coexistence'' rather than ``collaboration''. This thesis aims to develop an adaptive learning and control framework with a novel physical and data-driven approach towards a real collaborative robot. The first part focuses on online human motion prediction. A comprehensive study on various motion prediction techniques is presented, i...
Since the manipulation tasks for robotic systems become more and more complicated, multi-robot cooperation has been attracting much attention recently. Furthermore, under the trend of human-robot co-existence, collision-free motion control is now also desired on multi-robot groups. This dissertation aims to design a novel distributed optimal control framework to deal with multi-robot cooperative manipulation of rigid objects in dynamic environments. Besides object transportation, the control scheme also tackles obstacle avoidance, joint-space performance optimisation and internal force suppression. The proposed control framework has a two-layer structure, with a distributed optimisation algo...
In der Industrie laufen viele Prozesse zyklisch und damit wiederholend ab. Eine hohe Regelgüte ist hierbei unabdingbar. Daher kommen iterativ lernende Regelungsmethoden zum Einsatz, welche die Regelung des Prozesses zyklisch verbessern. In dieser Dissertation werden iterativ lernende modellprädiktive Regelungsverfahren vorgestellt. Die entwickelten Methoden ermöglichen durch ihre modellbasierte Struktur eine zyklische Steigerung der Regelgüte bei gleichzeitiger Berücksichtigung der Systembeschränkungen. Zyklische unbekannte Störungen und Dynamiken lassen sich hiermit iterativ erlernen und unterdrücken. Eine Robustifizierung der Verfahren gegenüber Messrauschen sowie Unsicherheiten w...
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Includes various departmental reports and reports of commissions.
The last one hundred years have seen many important achievements in the classical part of number theory. After the proof of the Prime Number Theorem in 1896, a quick development of analytical tools led to the invention of various new methods, like Brun's sieve method and the circle method of Hardy, Littlewood and Ramanujan; developments in topics such as prime and additive number theory, and the solution of Fermat’s problem. Rational Number Theory in the 20th Century: From PNT to FLT offers a short survey of 20th century developments in classical number theory, documenting between the proof of the Prime Number Theorem and the proof of Fermat's Last Theorem. The focus lays upon the part of number theory that deals with properties of integers and rational numbers. Chapters are divided into five time periods, which are then further divided into subject areas. With the introduction of each new topic, developments are followed through to the present day. This book will appeal to graduate researchers and student in number theory, however the presentation of main results without technicalities will make this accessible to anyone with an interest in the area.
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