Bor-Sen Chen

This book introduces the centralized robust H∞ team formation tracking control strategy of multi-unmanned aerial vehicle (multi-UAV) network system under intrinsic random fluctuation, time-varying delay and packet dropout in wireless communication, and external disturbance. A simple robust decentralized H∞ proportional-integral-derivative (PID) reference tracking network control strategy is introduced for practical applications of team formation of large-scale UAV under control saturation constraint, external disturbance, and vortex coupling. It provides practical design procedures based on linear matrix inequalities (LMIs) solvable via LMI TOOLBOX in MATLAB®. Features: Focuses on the stabilization of a QUAV under finite-time switching model control (SMC) Discusses robustness control design for formation tracking in UAV networks Introduces different robust centralized and decentralized H∞ attack-tolerant observer-based reference team formation tracking control of large-scale UAVs Reviews practical case studies in each chapter to introduce the design procedures Includes design examples of team formation of 25 quadrotor UAVs and a team formation example of five hybrid quadrotor/biped robot sub-teams This book is aimed at researchers and graduate students in control and electrical engineering.

Robust H∞ Team Formation Tracking Design Methods of Large-Scale UAV Networked Control Systems

Lihua Xie

Weihai Zhang

The H∞ control has been one of the important robust control approaches since the 1980s. This book extends the area to nonlinear stochastic H2/H∞ control, and studies more complex and practically useful mixed H2/H∞ controller synthesis rather than the pure H∞ control. Different from the commonly used convex optimization method, this book applies the Nash game approach to give necessary and sufficient conditions for the existence and uniqueness of the mixed H2/H∞ control. Researchers will benefit from our detailed exposition of the stochastic mixed H2/H∞ control theory, while practitioners can apply our efficient algorithms to address their practical problems.

Stochastic H2/H ∞ Control: A Nash Game Approach

Cheng-Wei Li

Big Mechanisms in Systems Biology: Big Data Mining, Network Modeling, and Genome-Wide Data Identification explains big mechanisms of systems biology by system identification and big data mining methods using models of biological systems. Systems biology is currently undergoing revolutionary changes in response to the integration of powerful technologies. Faced with a large volume of available literature, complicated mechanisms, small prior knowledge, few classes on the topics, and causal and mechanistic language, this is an ideal resource. This book addresses system immunity, regulation, infection, aging, evolution, and carcinogenesis, which are complicated biological systems with inconsistent findings in existing resources. These inconsistencies may reflect the underlying biology time-varying systems and signal transduction events that are often context-dependent, which raises a significant problem for mechanistic modeling since it is not clear which genes/proteins to include in models or experimental measurements. The book is a valuable resource for bioinformaticians and members of several areas of the biomedical field who are interested in an in-depth understanding on how to process and apply great amounts of biological data to improve research. Written in a didactic manner in order to explain how to investigate Big Mechanisms by big data mining and system identification Provides more than 140 diagrams to illustrate Big Mechanism in systems biology Presents worked examples in each chapter

Big Data Mining, Network Modeling, and Genome-Wide Data Identification

Big Mechanisms in Systems Biology

Game theory involves multi-person decision making and differential dynamic game theory has been widely applied to n-person decision making problems, which are stimulated by a vast number of applications. This book addresses the gap to discuss general stochastic n-person noncooperative and cooperative game theory with wide applications to control systems, signal processing systems, communication systems, managements, financial systems, and biological systems. H∞ game strategy, n-person cooperative and noncooperative game strategy are discussed for linear and nonlinear stochastic systems along with some computational algorithms developed to efficiently solve these game strategies.

Stochastic Game Strategies and their Applications

Cancer is a complex and heterogeneous (systematic) disease that exhibits high levels of robustness against various therapeutic interventions. Due to the complex, heterogeneous, and evolving nature of cancer, it is essential for a system-oriented view to be adopted in order to understand the pathogenic mechanism of cancer which will eventually lead to the drug discovery and treatment of patients. This book not only provides new insights from system modeling and large scale experimental data, but also will allow the reader to perceive the systematic characteristics of cancer under some micro-environments. Based on the systems biology method and genome-wide high throughput data, the book presents the PPI network, gene regulatory network and epigenetic network as an integrated genetic and epigenetic cellular network of lung cancer, bladder cancer, thyroid cancer, colorectal cancer, and liver cancer to investigate their core signaling pathways of the carcinogenesis process to identify their corresponding significant carcinogenic biomarkers as drug targets for systems drug discovery and design. Consequently, the book presents the design of a multiple-molecule drug that can target these biomarkers for the therapeutic treatment of cancer with fewer side effects.

Bor-Sen Chen

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