Nonlinear Pinning Control of Complex Dynamical Networks: Analysis and Applications

This book discusses two nonlinear control strategies for complex dynamical networks: sliding-mode control and inverse optimal control. It uses model-based approaches for identifying the unknown system and employs recurrent neural networks for learning. The book is organized into four sections, covering mathematical preliminaries, sliding-mode control, inverse optimal control, and applications.

Format: Paperback / softback
Length: 202 pages
Publication date: 25 September 2023
Publisher: Taylor & Francis Ltd

This comprehensive book delves into two innovative nonlinear control strategies for complex dynamical networks. Firstly, sliding-mode control is employed, followed by the inverse optimal control approach. Throughout the text, a model-based approach is considered in Chapters 3 and 5, while Chapters 4 and 6 focus on determining a model for the unknown system using a recurrent neural network, utilizing online extended Kalman filtering for learning.

The book is structured into four distinct sections. The first section provides a foundational overview, encompassing a brief review of complex networks and the pinning methodology. Additionally, sliding-mode control and inverse optimal control are introduced, along with an exploration of neural network structures, including high-order ones.

The second section presents comprehensive analysis and simulation results for sliding-mode control, encompassing both identical and non-identical nodes. The third section delves into the analysis and simulation of inverse optimal control, considering identical or non-identical nodes. Finally, the last section showcases practical applications of these schemes, utilizing gene regulatory networks and microgrids as illustrative examples.

By presenting these two nonlinear control strategies, this book offers valuable insights into the dynamic behavior of complex networks and provides practical tools for designing and optimizing control systems. It serves as a valuable resource for researchers, engineers, and practitioners in the field of control systems and nonlinear dynamics.

Weight: 421g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032020938