Huaqing Li – författare

Second-Order Consensus of Continuous-Time Multi-Agent Systems focuses on the characteristics and features of second-order agents, communication networks, and control protocols/algorithms in continuous consensus of multi-agent systems. The book provides readers with background on consensus control of multi-agent systems and introduces the intrinsic characteristics of second-order agents' behavior, including the development of continuous control protocols/algorithms over various types of underlying communication networks, as well as the implementation of computation- and communication-efficient strategies in the execution of protocols/algorithms. The book's authors also provide coverage of the frameworks of stability analysis, algebraic criteria and performance evaluation.

On this basis, the book provides an in-depth study of intrinsic nonlinear dynamics from agents' perspective, coverage of unbalanced directed topology, random switching topology, event-triggered communication, and random link failure, from a communication networks' perspective, as well as leader-following control, finite-time control, and global consensus control, from a protocols/algorithms' perspective. Finally, simulation results including practical application examples are presented to illustrate the effectiveness and the practicability of the control protocols and algorithms proposed in this book.

Introduces the latest and most advanced protocols and algorithms in second-order consensus of continuous time, multi-agent systems with various characteristics Provides readers with in-depth methods on how to construct the frameworks of stability analysis, algebraic criteria, and performance evaluation, thus helping users develop novel consensus control methods Includes systematic introductions and detailed implementations on how control protocols and algorithms solve problems in real world, second-order, multi-agent systems, including solutions for engineers in related fields

Decentralized Optimization in Networks: Algorithmic Efficiency and Privacy Preservation provides the reader with theoretical foundations, practical guidance, and solutions to decentralized optimization problems. The book demonstrates the application of decentralized optimization algorithms to enhance communication and computational efficiency, solve large-scale datasets, maintain privacy preservation, and address challenges in complex decentralized networks. The book covers key topics such as event-triggered communication, random link failures, zeroth-order gradients, variance-reduction, Polyak’s projection, stochastic gradient, random sleep, and differential privacy. It also includes simulations and practical examples to illustrate the algorithms' effectiveness and applicability in real-world scenarios.Introduces the latest and advanced algorithms in decentralized optimization of networked control systemsProposes effective strategies for efficient execution and privacy preservation in the development of decentralized optimization algorithmsConstructs the frameworks of convergence and complexity analysis, privacy, security proof, and performance evaluationIncludes systematic detailed implementations on how decentralized optimization algorithms solve the problems in real world systems: smart grid systems, online learning systems, wireless sensor systems, etc.Helps readers develop their own novel, decentralized optimization algorithms

Focusing on the natures and functions of agents, communication networks and algorithms in the context of distributed optimization for networked control systems, this book introduces readers to the background of distributed optimization;

Focusing on the natures and functions of agents, communication networks and algorithms in the context of distributed optimization for networked control systems, this book introduces readers to the background of distributed optimization;

This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning).

This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning).

This book provides the fundamental theory of distributed optimization, game and learning. This book is meant for the researcher and engineer who uses distributed optimization, game and learning theory in fields like dynamic economic dispatch, demand response management and PHEV routing of smart grids.

This book provides the fundamental theory of distributed optimization, game and learning. This book is meant for the researcher and engineer who uses distributed optimization, game and learning theory in fields like dynamic economic dispatch, demand response management and PHEV routing of smart grids.