Jiang / Prieur / Astolfi Trends in Nonlinear and Adaptive Control
Erscheinungsjahr 2021
ISBN: 978-3-030-74628-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
A Tribute to Laurent Praly for his 65th Birthday
E-Book, Englisch, 282 Seiten
Reihe: Intelligent Technologies and Robotics (R0)
ISBN: 978-3-030-74628-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book, published in honor of Professor Laurent Praly on the occasion of his 65th birthday, explores the responses of some leading international authorities to new challenges in nonlinear and adaptive control. The mitigation of the effects of uncertainty and nonlinearity – ubiquitous features of real-world engineering and natural systems – on closed-loop stability and robustness being of crucial importance, the contributions report the latest research into overcoming these difficulties in:
- autonomous systems;
- reset control systems;
- multiple-input–multiple-output nonlinear systems;
- input delays;
- partial differential equations;
- population games; and
- data-driven control.
Trends in Nonlinear and Adaptive Control presents research inspired by and related to Professor Praly’s lifetime of contributions to control theory and is a valuable additionto the literature of advanced control.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
1. Introduction.- 2. Almost Feedback Linearization via Dynamic Extension: A Paradigm for Robust Semiglobal Stabilization.- 3. Neural, Hybrid Methods in Nonlinear System Identification.- 4. One the Role of Well-Posedness in Homotopy Methods for the Stability Analysis of Nonlinear Feedback Systems.- 5. Design of Multi-Agent System via Blended Dynamics Approach.- 6. Robust Adaptive Attenuation of Unknown Output Disturbances.- 7. Delay-Adaptive Observer-Based Control for Linear Systems with Unknown Input Delays.- 8. Adaptive Control for Systems with Time-Varying Parameters.- 9. Multi-Armed Bandits Revisited via Adaptive Control.- 10. Contributions to the Problem of High-Gain Observer Design for Systems of PDEs.- 11. Robust Reinforcement Learning for Optimal Stationary Control of Linear Systems with Additive and Multiplicative Noises.




