Stabilization, Optimal and Robust Control, Aziz Belmiloudi

Автор: Lavretsky
Название: Robust and Adaptive Control
ISBN: 1447143957 ISBN-13(EAN): 9781447143956
Издательство: Springer
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Цена: 69870.00 T
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Описание: This book shows how to produce consistent and accurate controllers that operate in the presence of uncertainties and unforeseen events. It details how to convert theoretically-based control algorithms into realistic real-world aerospace applications.

Автор: Aziz Belmiloudi
Название: Stabilization, Optimal and Robust Control
ISBN: 1849967903 ISBN-13(EAN): 9781849967907
Издательство: Springer
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Цена: 278580.00 T
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Описание: The material here develops the robust control of infinite-dimensional dynamical systems derived from time-dependent coupled PDEs associated with boundary-value problems. Mathematical foundations are provided to keep the book accessible to the non-specialist.

Автор: Iasson Karafyllis; Zhong-Ping Jiang
Название: Stability and Stabilization of Nonlinear Systems
ISBN: 1447126475 ISBN-13(EAN): 9781447126478
Издательство: Springer
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Цена: 139310.00 T
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Описание: The subject of nonlinear control systems analysis has grown rapidly and this book provides a simple presentation of their stability and feedback stabilization. It enables the reader to learn and understand major techniques used in mathematical control theory.

Автор: Scheinker
Название: Model-Free Stabilization by Extremum Seeking
ISBN: 3319507893 ISBN-13(EAN): 9783319507897
Издательство: Springer
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Цена: 46570.00 T
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Описание: With this brief, the authors present algorithms for model-free stabilization of unstable dynamic systems. An extremum-seeking algorithm assigns the role of a cost function to the dynamic system’s control Lyapunov function (clf) aiming at its minimization. The minimization of the clf drives the clf to zero and achieves asymptotic stabilization. This approach does not rely on, or require knowledge of, the system model. Instead, it employs periodic perturbation signals, along with the clf. The same effect is achieved as by using clf-based feedback laws that profit from modeling knowledge, but in a time-average sense. Rather than use integrals of the systems vector field, we employ Lie-bracket-based (i.e., derivative-based) averaging.The brief contains numerous examples and applications, including examples with unknown control directions and experiments with charged particle accelerators. It is intended for theoretical control engineers and mathematicians, and practitioners working in various industrial areas and in robotics.