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Full Description
This book presents two nonlinear control strategies for complex dynamical networks. First, sliding-mode control is used, and then the inverse optimal control approach is employed. For both cases, model-based is considered in Chapter 3 and Chapter 5; then, Chapter 4 and Chapter 6 are based on determining a model for the unknow system using a recurrent neural network, using on-line extended Kalman filtering for learning.
The book is organized in four sections. The first one covers mathematical preliminaries, with a brief review for complex networks, and the pinning methodology. Additionally, sliding-mode control and inverse optimal control are introduced. Neural network structures are also discussed along with a description of the high-order ones. The second section presents the analysis and simulation results for sliding-mode control for identical as well as non-identical nodes. The third section describes analysis and simulation results for inverse optimal control considering identical or non-identical nodes. Finally, the last section presents applications of these schemes, using gene regulatory networks and microgrids as examples.
Contents
I. Analyses and Preliminaries: 1. Introduction. 2. Preliminaries. II. Sliding-Mode Control: 3. Model-Based Control. 4. Neural Model. III. Optimal Control: 5. Model- based Control. 6. Neural Model. IV. Applications: 7. Pinning Control for the p53-Mdm2 Network. 8. Secondary Control of Microgrids.
