Discrete Networked Dynamic Systems: Analysis and Performance

Length: 484 pages
Edition: 1
Language: English
Publisher: Academic Press
Publication Date: 2020-11-06
ISBN-10: 0128236981
ISBN-13: 9780128236987
Sales Rank: #10802012 (See Top 100 Books)

Discrete Networked Dynamic Systems: Analysis and Performance provides a high-level treatment of a general class of linear discrete-time dynamic systems interconnected over an information network, exchanging relative state measurements or output measurements. It presents a systematic analysis of the material and provides an account to the math development in a unified way.

The topics in this book are structured along four dimensions: Agent, Environment, Interaction, and Organization, while keeping global (system-centered) and local (agent-centered) viewpoints.

The focus is on the wide-sense consensus problem in discrete networked dynamic systems. The authors rely heavily on algebraic graph theory and topology to derive their results. It is known that graphs play an important role in the analysis of interactions between multiagent/distributed systems. Graph-theoretic analysis provides insight into how topological interactions play a role in achieving coordination among agents. Numerous types of graphs exist in the literature, depending on the edge set of G. A simple graph has no self-loop or edges. Complete graphs are simple graphs with an edge connecting any pair of vertices. The vertex set in a bipartite graph can be partitioned into disjoint non-empty vertex sets, whereby there is an edge connecting every vertex in one set to every vertex in the other set. Random graphs have fixed vertex sets, but the edge set exhibits stochastic behavior modeled by probability functions. Much of the studies in coordination control are based on deterministic/fixed graphs, switching graphs, and random graphs.

Cover image
Title page
Table of Contents
Copyright
Dedication
About the authors
Preface
Acknowledgement
Chapter 1: Mathematical background and examples
Abstract
1.1. Introduction
1.2. Mathematical background
1.3. Elements of algebraic graphs
1.4. Lyapunov stability
1.5. Minimum mean square estimate
1.6. Motivating problems
1.7. Notes
References
Chapter 2: Structural and performance patterns
Abstract
2.1. Introduction
2.2. Discrete networked dynamic systems
2.3. System properties
2.4. Controllability Gramian
2.5. Observability properties of discrete networked dynamic systems
2.6. Index of homogeneity and heterogeneity
2.7. Agent feedback stability
2.8. Synthesis schemes of discrete networked dynamic systems
2.9. H∞ performance and robust topology design
2.10. Notes
References
Chapter 3: Consensus of systems over graphs
Abstract
3.1. Dynamic consensus protocol
3.2. Multiagent systems with diverse time delays
3.3. Decentralized consensus prediction
3.4. Performance of agreement protocol
3.5. Scalable consensus conditions
3.6. Notes
References
Chapter 4: Energy-based cooperative control
Abstract
4.1. Dissipative cooperative output synchronization
4.2. Passivity analysis of time delay systems
4.3. Consensus tracking of saturated systems
4.4. Notes
References
Chapter 5: Performance of consensus algorithms
Abstract
5.1. Introduction
5.2. The agreement algorithm
5.3. Performance and robustness of averaging algorithms
5.4. Leader following consensus
5.5. Stochastic approximation algorithms
5.6. Notes
References
Chapter 6: Event-based coordination control
Abstract
6.1. Event-based tracking control
6.2. Discrete two-timescale systems
6.3. Networks of two-timescale systems
6.4. Consensus of multiagent delay systems with adversaries
6.5. Notes
References
Chapter 7: Advanced approaches to multiagent coordination
Abstract
7.1. Synchronization of stochastic dynamic networks
7.2. Observer-based consensus protocols
7.3. Event-based tracking control
7.4. Robust output regulation
7.5. Distributed networked control
7.6. Notes
References
Chapter 8: State estimation techniques
Abstract
8.1. Asynchronous multirate multismart sensors
8.2. Nonlinear state estimation
8.3. Distributed filtering with saturation
8.4. Notes
References
Chapter 9: Advanced distributed filtering
Abstract
9.1. Self-tuning Kalman filtering
9.2. Kalman filtering with intermittent communications
9.3. Information-based algorithms
9.4. Covariance intersection
9.5. Information-based covariance intersection filter
9.6. Simulation example 9.2
9.7. Notes
References
Index