Adaptive Internal Model Control

Softcover Nachdruck of the original 1. Auflage 1998
ISBN: 978-1-4471-1042-2
Verlag: Springer

Buch, Englisch, 153 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 277 g

Reihe: Advances in Industrial Control

Adaptive Internal Model Control is a methodology for the design and analysis of adaptive internal model control schemes with provable guarantees of stability and robustness. Written in a self-contained tutorial fashion, this research monograph successfully brings the latest theoretical advances in the design of robust adaptive systems to the realm of industrial applications. It provides a theoretical basis for analytically justifying some of the reported industrial successes of existing adaptive internal model control schemes, and enables the reader to synthesise adaptive versions of their own favourite robust internal model control scheme by combining it with a robust adaptive law. The net result is that earlier empirical IMC designs can now be systematically robustified or replaced altogether by new designs with assured guarantees of stability and robustness.

Zielgruppe

Research

Autoren/Hrsg.

Datta, Aniruddha

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Verfahrenstechnik, Chemieingenieurwesen
• Technische Wissenschaften Technik Allgemein Modellierung & Simulation
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie

Inhaltsverzeichnis

1. Introduction.- 2. Mathematical Preliminaries.- 3. Internal Model Control Schemes.- 4. On-line Parameter Estimation.- 5. Adaptive Internal Model Control Schemes.- 6. Robust Parameter Estimation.- 7. Robust Adaptive IMC Schemes.- 8. Conclusion.- A. The YJBK Parametrization of All Stabilizing Controllers.- B. Optimization Using the Gradient Method.