1.

Record Nr.

UNINA9910672447403321

Autore

Yu Qiongxia

Titolo

Predictive Learning Control for Unknown Nonaffine Nonlinear Systems : Theory and Applications / / Qiongxia Yu [and four others]

Pubbl/distr/stampa

Singapore : , : Springer, Springer Nature Singapore Pte Ltd., , [2023]

©2023

ISBN

981-19-8857-9

Edizione

[1st ed. 2023.]

Descrizione fisica

1 online resource (219 pages)

Collana

Intelligent Control and Learning Systems Series ; ; Volume 8

Disciplina

003.75

Soggetti

Nonlinear systems

Predictive control

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references.

Nota di contenuto

Chapter 1: Introduction -- Chapter 2 Predictive Learning Control for Unknown Systems -- Chapter 3 Constrained Predictive Learning Control -- Chapter 4 Predictive Learning Control for Systems with Varying Trial Lengths  -- Chapter 5 Predictive Learning Control for Systems with Unknown Time Delay -- Chapter 6 Predictive Learning Control for Systems with Full Available States -- Chapter 7 Predictive Learning Control for Systems with Unavailable States -- Chapter 8 High-Speed Train Automatic Operation Systems -- Chapter 9 Fundamental Two-Region Urban Road Networks -- Chapter 10 Complex Large-Scale Multi-Region Urban Traffic Systems -- Chapter 11 Conclusions.

Sommario/riassunto

This book investigates both theory and various applications of predictive learning control (PLC) which is an advanced technology for complex nonlinear systems. To avoid the difficult modeling problem for complex nonlinear systems, this book begins with the design and theoretical analysis of PLC method without using mechanism model information of the system, and then a series of PLC methods is designed that can cope with system constraints, varying trial lengths, unknown time delay, and available and unavailable system states sequentially. Applications of the PLC on both railway and urban road transportation systems are also studied. The book is intended for researchers, engineers, and graduate students who are interested in predictive control, learning control, intelligent transportation systems



and related fields.