Modeling And Control For Micro Nano Devices And Systems

Micro/nano-scale engineering—especially the design and implementation of ultra-fast and ultra-scale energy devices, sensors, and cellular and molecular systems—remains a daunting challenge. Modeling and control has played an essential role in many technological breakthroughs throughout the course of history. Therefore, the need for a practical guide to modeling and control for micro/nano-scale devices and systems has emerged. The first edited volume to address this rapidly growing field, Modeling and Control for Micro/Nano Devices and Systems gives control engineers, lab managers, high-tech researchers, and graduate students easy access to the expert contributors’ cutting-edge knowledge of micro/nanotechnology, energy, and bio-systems. The editors offer an integrated view from theory to practice, covering diverse topics ranging from micro/nano-scale sensors to energy devices and control of biology systems in cellular and molecular levels. The book also features numerous case studies for modeling of micro/nano devices and systems, and explains how the models can be used for control and optimization purposes. Readers benefit from learning the latest modeling techniques for micro/nano-scale devices and systems, and then applying those techniques to their own research and development efforts.

Micro- and nanosystems represent an area of major scientific and technological opportunity and challenge, with actual and potential applications in almost all fields of human activity. The aim of this book is to present the central concepts of dynamic control systems (modeling, estimation, observation, identification, feedback control) and to show how they can be adapted and applied to the development of novel very small-scale systems and their associated human interfaces. The application fields presented here come from micro- and nano-robotics, biochips, near-field microscopy (AFM and STM) and nano-systems networks. Alina Voda has assembled contributions from leading experts at top research universities to produce the first overview of the major role that control systems science will play in the development of micro and nano-science and technologies.

Smart Materials-Based Actuators at the Micro/Nano-Scale: Characterization, Control, and Applications gives a state of the art of emerging techniques to the characterization and control of actuators based on smart materials working at the micro/nano scale. The book aims to characterize some commonly used structures based on piezoelectric and electroactive polymeric actuators and also focuses on various and emerging techniques employed to control them. This book also includes two of the most emerging topics and applications: nanorobotics and cells micro/nano-manipulation.

Doubly Fed Induction Generators: Control for Wind Energy provides a detailed source of information on the modeling and design of controllers for the doubly fed induction generator (DFIG) used in wind energy applications. Focusing on the use of nonlinear control techniques, this book: Discusses the main features and advantages of the DFIG Describes key theoretical fundamentals and the DFIG mathematical model Develops controllers using inverse optimal control, sliding modes, and neural networks Devises an improvement to add robustness in the presence of parametric variations Details the results of real-time implementations All controllers presented in the book are tested in a laboratory prototype. Comparisons between the controllers are made by analyzing statistical measures applied to the control objectives.

Distributed controller design is generally a challenging task, especially for multi-agent systems with complex dynamics, due to the interconnected effect of the agent dynamics, the interaction graph among agents, and the cooperative control laws. Cooperative Control of Multi-Agent Systems: A Consensus Region Approach offers a systematic framework for designing distributed controllers for multi-agent systems with general linear agent dynamics, linear agent dynamics with uncertainties, and Lipschitz nonlinear agent dynamics. Beginning with an introduction to cooperative control and graph theory, this monograph: Explores the consensus control problem for continuous-time and discrete-time linear multi-agent systems Studies the H∞ and H2 consensus problems for linear multi-agent systems subject to external disturbances Designs distributed adaptive consensus protocols for continuous-time linear multi-agent systems Considers the distributed tracking control problem for linear multi-agent systems with a leader of nonzero control input Examines the distributed containment control problem for the case with multiple leaders Covers the robust cooperative control problem for multi-agent systems with linear nominal agent dynamics subject to heterogeneous matching uncertainties Discusses the global consensus problem for Lipschitz nonlinear multi-agent systems Cooperative Control of Multi-Agent Systems: A Consensus Region Approach provides a novel approach to designing distributed cooperative protocols for multi-agent systems with complex dynamics. The proposed consensus region decouples the design of the feedback gain matrices of the cooperative protocols from the communication graph and serves as a measure for the robustness of the protocols to variations of the communication graph. By exploiting the decoupling feature, adaptive cooperative protocols are presented that can be designed and implemented in a fully distributed fashion.

Networked Control Systems (NCSs) are spatially distributed systems for which the communication between sensors, actuators and controllers is realized by a shared (wired or wireless) communication network. NCSs offer several advantages, such as reduced installation and maintenance costs, as well as greater flexibility, over conventional control systems in which parts of control loops exchange information via dedicated point-to-point connections. The principal goal of this book is to present a coherent and versatile framework applicable to various settings investigated by the authors over the last several years. This framework is applicable to nonlinear time-varying dynamic plants and controllers with delayed dynamics; a large class of static, dynamic, probabilistic and priority-oriented scheduling protocols; delayed, noisy, lossy and intermittent information exchange; decentralized control problems of heterogeneous agents with time-varying directed (not necessarily balanced) communication topologies; state- and output-feedback; off-line and on-line intermittent feedback; optimal intermittent feedback through Approximate Dynamic Programming (ADP) and Reinforcement Learning (RL); and control systems with exogenous disturbances and modeling uncertainties.

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.