Download or read book Stochastic Modeling Optimization and Data driven Adaptive Control with Applications in Cloud Computing and Cyber Security written by Yue Tan and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data has flown into every sector of the global economy ranging from social networks to online business to finance to medicine. With the rapid growth of data in many applications in the society, operations research (OR) professionals must shift to a broader view of developing analytical solutions characterized by the integrated use of data, processes and systems. Classical stochastic modeling, although proved to be useful in many traditional application areas (e.g. call centers, manufacturing systems), few works have been done in new applications arising from big data. Existing methods are lack of integration between data and modeling, recent development in adaptive control fails to address these new applications. In this dissertation, we aim to fill these gaps by developing new stochastic modeling, optimization and data-driven adaptive control approaches for managerial problems such as the resource provisioning of cloud computing and password management in cyber security systems.
Download or read book Handbook of Dynamic Data Driven Applications Systems written by Frederica Darema and published by Springer Nature. This book was released on 2023-10-16 with total page 937 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitation of the DDDAS paradigm and the ensuing frameworks, through the examples and case studies presented, either within their own field or other fields of study. As in the first volume, the chapters in this book reflect research work conducted over the years starting in the 1990’s to the present. Here, the theory and application content are considered for: Foundational Methods Materials Systems Structural Systems Energy Systems Environmental Systems: Domain Assessment & Adverse Conditions/Wildfires Surveillance Systems Space Awareness Systems Healthcare Systems Decision Support Systems Cyber Security Systems Design of Computer Systems The readers of this book series will benefit from DDDAS theory advances such as object estimation, information fusion, and sensor management. The increased interest in Artificial Intelligence (AI), Machine Learning and Neural Networks (NN) provides opportunities for DDDAS-based methods to show the key role DDDAS plays in enabling AI capabilities; address challenges that ML-alone does not, and also show how ML in combination with DDDAS-based methods can deliver the advanced capabilities sought; likewise, infusion of DDDAS-like approaches in NN-methods strengthens such methods. Moreover, the “DDDAS-based Digital Twin” or “Dynamic Digital Twin”, goes beyond the traditional DT notion where the model and the physical system are viewed side-by-side in a static way, to a paradigm where the model dynamically interacts with the physical system through its instrumentation, (per the DDDAS feed-back control loop between model and instrumentation).
Download or read book Safe Adaptive Control written by Margareta Stefanovic and published by Springer Science & Business Media. This book was released on 2011-02-10 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Safe Adaptive Control gives a formal and complete algorithm for assuring the stability of a switched control system when at least one of the available candidate controllers is stabilizing. The possibility of having an unstable switched system even in the presence of a stabilizing candidate controller is demonstrated by referring to several well-known adaptive control approaches, where the system goes unstable when a large mismatch between the unknown plant and the available models exists ("plant-model mismatch instability"). Sufficient conditions for this possibility to be avoided are formulated, and a "recipe" to be followed by the control system designer to guarantee stability and desired performance is provided. The problem is placed in a standard optimization setting. Unlike the finite controller sets considered elsewhere, the candidate controller set is allowed to be continuously parametrized so that it can deal with plants with a very large range of uncertainties.
Download or read book Intelligent Control written by Kaushik Das Sharma and published by Springer. This book was released on 2018-08-28 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses systematic designs of stable adaptive fuzzy logic controllers employing hybridizations of Lyapunov strategy-based approaches/H∞ theory-based approaches and contemporary stochastic optimization techniques. The text demonstrates how candidate stochastic optimization techniques like Particle swarm optimization (PSO), harmony search (HS) algorithms, covariance matrix adaptation (CMA) etc. can be utilized in conjunction with the Lyapunov theory/H∞ theory to develop such hybrid control strategies. The goal of developing a series of such hybridization processes is to combine the strengths of both Lyapunov theory/H∞ theory-based local search methods and stochastic optimization-based global search methods, so as to attain superior control algorithms that can simultaneously achieve desired asymptotic performance and provide improved transient responses. The book also demonstrates how these intelligent adaptive control algorithms can be effectively utilized in real-life applications such as in temperature control for air heater systems with transportation delay, vision-based navigation of mobile robots, intelligent control of robot manipulators etc.
Download or read book Adaptive Stochastic Optimization Techniques with Applications written by James A. Momoh and published by CRC Press. This book was released on 2015-12-02 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive Stochastic Optimization Techniques with Applications provides a single, convenient source for state-of-the-art information on optimization techniques used to solve problems with adaptive, dynamic, and stochastic features. Presenting modern advances in static and dynamic optimization, decision analysis, intelligent systems, evolutionary pro
Download or read book Modeling Stochastic Control Optimization and Applications written by George Yin and published by Springer. This book was released on 2019-07-16 with total page 599 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume collects papers, based on invited talks given at the IMA workshop in Modeling, Stochastic Control, Optimization, and Related Applications, held at the Institute for Mathematics and Its Applications, University of Minnesota, during May and June, 2018. There were four week-long workshops during the conference. They are (1) stochastic control, computation methods, and applications, (2) queueing theory and networked systems, (3) ecological and biological applications, and (4) finance and economics applications. For broader impacts, researchers from different fields covering both theoretically oriented and application intensive areas were invited to participate in the conference. It brought together researchers from multi-disciplinary communities in applied mathematics, applied probability, engineering, biology, ecology, and networked science, to review, and substantially update most recent progress. As an archive, this volume presents some of the highlights of the workshops, and collect papers covering a broad range of topics.
Download or read book Identification and Stochastic Adaptive Control written by Han-fu Chen and published by Springer Science & Business Media. This book was released on 1991-11 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: Identifying the input-output relationship of a system or discovering the evolutionary law of a signal on the basis of observation data, and applying the constructed mathematical model to predicting, controlling or extracting other useful information constitute a problem that has been drawing a lot of attention from engineering and gaining more and more importance in econo metrics, biology, environmental science and other related areas. Over the last 30-odd years, research on this problem has rapidly developed in various areas under different terms, such as time series analysis, signal processing and system identification. Since the randomness almost always exists in real systems and in observation data, and since the random process is sometimes used to model the uncertainty in systems, it is reasonable to consider the object as a stochastic system. In some applications identification can be carried out off line, but in other cases this is impossible, for example, when the structure or the parameter of the system depends on the sample, or when the system is time-varying. In these cases we have to identify the system on line and to adjust the control in accordance with the model which is supposed to be approaching the true system during the process of identification. This is why there has been an increasing interest in identification and adaptive control for stochastic systems from both theorists and practitioners.
Download or read book Cloud Control Systems written by Magdi S. Mahmoud and published by Academic Press. This book was released on 2020-01-14 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cloud Control Systems: Analysis, Design and Estimation introduces readers to the basic definitions and various new developments in the growing field of cloud control systems (CCS). The book begins with an overview of cloud control systems (CCS) fundamentals, which will help beginners to better understand the depth and scope of the field. It then discusses current techniques and developments in CCS, including event-triggered cloud control, predictive cloud control, fault-tolerant and diagnosis cloud control, cloud estimation methods, and secure control/estimation under cyberattacks. This book benefits all researchers including professors, postgraduate students and engineers who are interested in modern control theory, robust control, multi-agents control. Offers insights into the innovative application of cloud computing principles to control and automation systems Provides an overview of cloud control systems (CCS) fundamentals and introduces current techniques and developments in CCS Investigates distributed denial of service attacks, false data injection attacks, resilient design under cyberattacks, and safety assurance under stealthy cyberattacks
Download or read book Identification and Stochastic Adaptive Control written by Hanfu Chen and published by . This book was released on 1991-01-01 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Stochastic Adaptive Control Results and Simulations written by Alexis Aloneftis and published by Springer. This book was released on 1987 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theme of this monograph is the adaptive control of systems in a stochastic environment and, more precisely, the study of the tracking problem for ARMAX SISO stochastic systems with time invariant and time varying parameters. Results of simultaneous tracking and parameter identification are included. The author has aimed to (1) provide a reasonably self-contained and up-to-date exposition of the tracking problem after having properly placed it amongst numerous ideas, approaches, and subproblems related to adaptive control, (2) display computer simulation results and discuss their comparative behaviour, (3) introduce a new approach to the stochastic adaptive control with promising results, and (4) qualitatively discuss the adaptive control problem in the hope of improving our understanding of it, stimulate the informed reader to come up with new ideas, and attract newcomers to its study. The reader is assumed to have studied control systems at the graduate level and to have a reasonably good grasp of basic probability theory. Apart from its educational value to the adaptive control student, it is hoped that the accumulation of scattered results and their computer simulation, as well as an extensive reference section will attract the active researcher in this field.
Download or read book Stochastic Networked Control Systems written by Serdar Yüksel and published by Springer Science & Business Media. This book was released on 2013-05-21 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networked control systems are increasingly ubiquitous today, with applications ranging from vehicle communication and adaptive power grids to space exploration and economics. The optimal design of such systems presents major challenges, requiring tools from various disciplines within applied mathematics such as decentralized control, stochastic control, information theory, and quantization. A thorough, self-contained book, Stochastic Networked Control Systems: Stabilization and Optimization under Information Constraints aims to connect these diverse disciplines with precision and rigor, while conveying design guidelines to controller architects. Unique in the literature, it lays a comprehensive theoretical foundation for the study of networked control systems, and introduces an array of concrete tools for work in the field. Salient features included: · Characterization, comparison and optimal design of information structures in static and dynamic teams. Operational, structural and topological properties of information structures in optimal decision making, with a systematic program for generating optimal encoding and control policies. The notion of signaling, and its utilization in stabilization and optimization of decentralized control systems. · Presentation of mathematical methods for stochastic stability of networked control systems using random-time, state-dependent drift conditions and martingale methods. · Characterization and study of information channels leading to various forms of stochastic stability such as stationarity, ergodicity, and quadratic stability; and connections with information and quantization theories. Analysis of various classes of centralized and decentralized control systems. · Jointly optimal design of encoding and control policies over various information channels and under general optimization criteria, including a detailed coverage of linear-quadratic-Gaussian models. · Decentralized agreement and dynamic optimization under information constraints. This monograph is geared toward a broad audience of academic and industrial researchers interested in control theory, information theory, optimization, economics, and applied mathematics. It could likewise serve as a supplemental graduate text. The reader is expected to have some familiarity with linear systems, stochastic processes, and Markov chains, but the necessary background can also be acquired in part through the four appendices included at the end. · Characterization, comparison and optimal design of information structures in static and dynamic teams. Operational, structural and topological properties of information structures in optimal decision making, with a systematic program for generating optimal encoding and control policies. The notion of signaling, and its utilization in stabilization and optimization of decentralized control systems. · Presentation of mathematical methods for stochastic stability of networked control systems using random-time, state-dependent drift conditions and martingale methods. · Characterization and study of information channels leading to various forms of stochastic stability such as stationarity, ergodicity, and quadratic stability; and connections with information and quantization theories. Analysis of various classes of centralized and decentralized control systems. · Jointly optimal design of encoding and control policies over various information channels and under general optimization criteria, including a detailed coverage of linear-quadratic-Gaussian models. · Decentralized agreement and dynamic optimization under information constraints. This monograph is geared toward a broad audience of academic and industrial researchers interested in control theory, information theory, optimization, economics, and applied mathematics. It could likewise serve as a supplemental graduate text. The reader is expected to have some familiarity with linear systems, stochastic processes, and Markov chains, but the necessary background can also be acquired in part through the four appendices included at the end.
Download or read book Stochastic Theory and Adaptive Control written by T. E. Duncan and published by Springer. This book was released on 1992 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: This workshop on stochastic theory and adaptive control assembled many of the leading researchers on stochastic control and stochastic adaptive control to increase scientific exchange and cooperative research between these two subfields of stochastic analysis. The papers included in the proceedings include survey and research. They describe both theoretical results and applications of adaptive control. There are theoretical results in identification, filtering, control, adaptive control and various other related topics. Some applications to manufacturing systems, queues, networks, medicine and other topics are gien.
Download or read book Data driven Control and Planning for Uncertain Complex Systems written by Benjamin John Gravell and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) has emerged as a front-runner in the race to imbue machines with artificial intelligence, driven by advances in parallel computing hardware, deep neural network architectures, and large data sets. Simultaneously, advances in actuator and sensor technology have brought about platforms with enormous potential to automate menial, dangerous, and expensive tasks such as autonomous driving. As RL leaps from virtual to physical environments, a new challenge appears: safety becomes far more critical. Despite uncertainty about the effects of decisions, which is always present due to noise, errors, and changing environments, control policies must nonetheless direct the evolution of the system towards the goal and away from danger. The fundamental goal of this dissertation is to realize the potential of RL in modern complex systems, contending with challenges posed by dynamics and feedback in the face of uncertainty, by establishing performance and safety guarantees. Despite rapid recent progress, there is still a large gap between theory and practice for optimal control of even relatively simple dynamical systems affected by randomness whose dynamics and statistics are unknown. As a steppingstone towards analysis of more complicated systems, this dissertation focuses on linear systems with multiplicative noise, a stochastic system representation with several practical applications, and linear quadratic control tasks for such systems, useful baselines for which optimal policies can be efficiently computed. This dissertation begins by establishing fundamental connections between stochastic stability and robust stability, formally demonstrating the utility of multiplicative noise as a tool for inducing robustness to parametric uncertainty in dynamic models. Building on this motivating result, RL algorithms across the model-based / model-free continuum that learn optimal policies only from observed data, without explicit access to dynamics or noise statistics governing the system, are developed and analyzed. First, modelfree policy optimization methods, which directly tune policy parameters, are proved to converge at a linear rate with quantified sample complexity polynomial in problemdependent quantities, and can, with appropriate regularization, achieve actuator and sensor sparsity for economical control of networks without sacrificing stability or performance. Second, approximate dynamic programming methods, which have mixed modelfree/based character and learn intermediate value functions, are shown to achieve a fast cubic rate of convergence with a novel midpoint formulation, and obtain policies with enhanced robustness properties when applied to stochastic dynamic games. Third, modelbased system identification methods, which estimate a dynamic model, are proved to learn the dynamics and noise statistics of multiplicative noise systems with quantified error that scales inversely with the square root of the amount of observed data, and are incorporated into adaptive control schemes that obtain robustness against actual uncertainty accrued from model estimation errors. To tractably generate safe trajectories, which are tracked by these data-driven policies, motion planning techniques are developed that actively avoid collisions and account for disturbance profiles with unknown distributions via distributionally robust checks. Throughout, examples are provided to clarify concepts, simulations and experiments are performed to validate theoretical results, and computer code is made available to promote reproducibility and future discoveries.
Download or read book Safe Adaptive Control written by Margareta Stefanovic and published by . This book was released on 2011-03-30 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Stochastic Learning and Optimization written by Xi-Ren Cao and published by Springer. This book was released on 2010-10-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Performance optimization is vital in the design and operation of modern engineering systems, including communications, manufacturing, robotics, and logistics. Most engineering systems are too complicated to model, or the system parameters cannot be easily identified, so learning techniques have to be applied. This book provides a unified framework based on a sensitivity point of view. It also introduces new approaches and proposes new research topics within this sensitivity-based framework. This new perspective on a popular topic is presented by a well respected expert in the field.
Download or read book Networked Control Systems written by Magdi S. Mahmoud and published by Butterworth-Heinemann. This book was released on 2019-02-09 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networked Control Systems: Cloud Control and Secure Control explores new technological developments in networked control systems (NCS), including new techniques, such as event-triggered, secure and cloud control. It provides the fundamentals and underlying issues of networked control systems under normal operating environments and under cyberphysical attack. The book includes a critical examination of the principles of cloud computing, cloud control systems design, the available techniques of secure control design to NCS’s under cyberphysical attack, along with strategies for resilient and secure control of cyberphysical systems. Smart grid infrastructures are also discussed, providing diagnosis methods to analyze and counteract impacts. Finally, a series of practical case studies are provided to cover a range of NCS’s. This book is an essential resource for professionals and graduate students working in the fields of networked control systems, signal processing and distributed estimation. Provides coverage of cloud-based approaches to control systems and secure control methodologies to protect cyberphysical systems against various types of malicious attacks Provides an overview of control research literature and explores future developments and solutions Includes case studies that offer solutions for issues with modeling, quantization, packet dropout, time delay and communication constraints
Download or read book Recent Developments in Model Based and Data Driven Methods for Advanced Control and Diagnosis written by Didier Theilliol and published by Springer Nature. This book was released on 2023-07-15 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book consists of recent works on several axes either with a more theoretical nature or with a focus on applications, which will span a variety of up-to-date topics in the field of systems and control. The main market area of the contributions include: Advanced fault-tolerant control, control reconfiguration, health monitoring techniques for industrial systems, data-driven diagnosis methods, process supervision, diagnosis and control of discrete-event systems, maintenance and repair strategies, statistical methods for fault diagnosis, reliability and safety of industrial systems artificial intelligence methods for control and diagnosis, health-aware control design strategies, advanced control approaches, deep learning-based methods for control and diagnosis, reinforcement learning-based approaches for advanced control, diagnosis and prognosis techniques applied to industrial problems, Industry 4.0 as well as instrumentation and sensors. These works constitute advances in the aforementioned scientific fields and will be used by graduate as well as doctoral students along with established researchers to update themselves with the state of the art and recent advances in their respective fields. As the book includes several applicative studies with several multi-disciplinary contributions (deep learning, reinforcement learning, model-based/data-based control etc.), the book proves to be equally useful for the practitioners as well industrial professionals.
