Tengfei Liu's Books

Robust Event-Triggered Control of Nonlinear Systems

By: Tengfei Liu,Pengpeng Zhang,Zhong-Ping Jiang

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Robust Event-Triggered Control of Nonlinear Systems

By: Tengfei Liu,Pengpeng Zhang,Zhong-Ping Jiang

This book presents a study on the novel concept of "event-triggered control of nonlinear systems subject to disturbances", discussing the theory and practical applications. Richly illustrated, it is a valuable resource for researchers, engineers and graduate students in automation engineering who wish to learn the theories, technologies, and applications of event-triggered control of nonlinear systems.

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Page Count

264

Publisher

Springer Nature

Published Date

ISBN 10

9811550131

ISBN 13

9789811550133

Nonlinear Control of Dynamic Networks

By: Tengfei Liu,Zhong-Ping Jiang,David J. Hill

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Nonlinear Control of Dynamic Networks

By: Tengfei Liu,Zhong-Ping Jiang,David J. Hill

Significant progress has been made on nonlinear control systems in the past two decades. However, many of the existing nonlinear control methods cannot be readily used to cope with communication and networking issues without nontrivial modifications. For example, small quantization errors may cause the performance of a "well-designed" nonlinear control system to deteriorate. Motivated by the need for new tools to solve complex problems resulting from smart power grids, biological processes, distributed computing networks, transportation networks, robotic systems, and other cutting-edge control applications, Nonlinear Control of Dynamic Networks tackles newly arising theoretical and real-world challenges for stability analysis and control design, including nonlinearity, dimensionality, uncertainty, and information constraints as well as behaviors stemming from quantization, data-sampling, and impulses. Delivering a systematic review of the nonlinear small-gain theorems, the text: Supplies novel cyclic-small-gain theorems for large-scale nonlinear dynamic networks Offers a cyclic-small-gain framework for nonlinear control with static or dynamic quantization Contains a combination of cyclic-small-gain and set-valued map designs for robust control of nonlinear uncertain systems subject to sensor noise Presents a cyclic-small-gain result in directed graphs and distributed control of nonlinear multi-agent systems with fixed or dynamically changing topology Based on the authors’ recent research, Nonlinear Control of Dynamic Networks provides a unified framework for robust, quantized, and distributed control under information constraints. Suggesting avenues for further exploration, the book encourages readers to take into consideration more communication and networking issues in control designs to better handle the arising challenges.

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Page Count

347

Publisher

CRC Press

Published Date

ISBN 10

1351831704

ISBN 13

9781351831703

Inverse Design Methods for the Built Environment

By: Qingyan Chen,Zhiqiang Zhai,Xueyi You,Tengfei Zhang

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Inverse Design Methods for the Built Environment

By: Qingyan Chen,Zhiqiang Zhai,Xueyi You,Tengfei Zhang

The inverse design approach is new to the built environment research and design community, though it has been used in other industries including automobile and airplane design. This book, from some of the pioneers of inverse design applications in the built environment, introduces the basic principles of inverse design and the specific techniques that can be applied to built environment systems. The authors' inverse design concept uses the desired enclosed environment as the design objective and inversely determines the systems required to achieve the objective. The book discusses a number of backward and forward methods for inverse design. Backward methods, such as the quasi-reversibility method, the pseudo-reversibility method, and the regularized inverse matrix method, can be used to identify contaminant sources in an enclosed environment. However, these methods cannot be used to inversely design a desired indoor environment. Forward methods, such as the computational-fluid-dynamics (CFD)-based genetic algorithm (GA) method, the CFD-based adjoint method, the CFD-based artificial neural network (ANN) method, and the CFD-based proper orthogonal decomposition (POD) method, show the promise in the inverse design of airflow and heat transfer in an enclosed environment. The book describes the fundamentals of the methods for beginners, provides exciting design examples for the reader to duplicate, discusses the pros and cons of each design method and points out the knowledge gaps for further development.

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