Polynomial Approximation For Data Driven System Analysis And Control Of Nonlinear Systems
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Polynomial Approximation For Data Driven System Analysis And Control Of Nonlinear Systems
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Author : Tim Martin
language : en
Publisher: Logos Verlag Berlin GmbH
Release Date : 2024-12-15
Polynomial Approximation For Data Driven System Analysis And Control Of Nonlinear Systems written by Tim Martin and has been published by Logos Verlag Berlin GmbH this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-15 with Technology & Engineering categories.
This thesis presents data-driven methods for nonlinear systems, enabling the verification of system-theoretical properties and the design of state feedbacks based on measured trajectories. Despite noisy data, the developed methods provide rigorous guarantees and leverage convex optimization. Classical control techniques require a mathematical model of the system dynamics, which derivation from first principles often demands expert knowledge or is time-consuming. In contrast, data-based control methods determine system properties and controllers from system trajectories. Whereas recent developments address linear systems, dynamical systems are generally nonlinear in practice. Therefore, this thesis first introduces a data-based system representation for unknown polynomial systems to determine dissipativity and integral quadratic constraints via sum-of-squares optimization. The second part of the thesis establishes a polynomial representation of nonlinear systems based on polynomial interpolation. Due to the unknown interpolation polynomial, a set of polynomials containing the actual interpolation polynomial is deduced from noisy data. This set, along with a polynomial bound on the approximation error, forms the basis for determining dissipativity properties and designing state feedbacks with stability guarantees utilizing robust control techniques and sum-of-squares relaxation.
Hybrid And Networked Dynamical Systems
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Author : Romain Postoyan
language : en
Publisher: Springer Nature
Release Date : 2024-03-20
Hybrid And Networked Dynamical Systems written by Romain Postoyan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-20 with Science categories.
Hybrid and Networked Dynamical Systems treats a class of systems that is ubiquitous in everyday life. From energy grids to fleets of robots or vehicles to social networks to biological networks, the same scenario arises: dynamical units interact locally through a connection graph to achieve a global task. The book shows how analysis and design tools can be adapted for control applications that combine the effects of network-induced interactions and hybrid dynamics with complex results. Following a scene-setting introduction, the remaining 12 chapters of the book are divided into three parts and provide a unique opportunity to describe the big picture that is the culmination of years of recent research activity. The contributing authors expand on their ideas at greater length than is possible in an archival research paper and use in-depth examples to illustrate their theoretical work. The widespread importance of hybrid and networked systems means that the book is of significant interest to academic researchers working in applied mathematics, control, and electrical, mechanical and chemical engineering and to their industrial counterparts.
Analysis And Design Of Aperiodic Sampling In Control Systems
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Author : Stefan Wildhagen
language : en
Publisher: Logos Verlag Berlin GmbH
Release Date : 2023-11-02
Analysis And Design Of Aperiodic Sampling In Control Systems written by Stefan Wildhagen and has been published by Logos Verlag Berlin GmbH this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-02 with categories.
This thesis addresses data-driven analysis as well as prediction-based design of aperiodic sampling in control systems. In many modern control applications, the traditional view of sampling and controlling in a periodic fashion must be abandoned due to varying transmission delays, data loss or uneven computational latencies. The effect of these undesired aperiodicities on the closed loop must thus be analyzed. In addition, a targeted aperiodic design of the sampling pattern, e.g. via event-triggered control (ETC), often allows for a better usage of bandwidth than periodic concepts. We consider both perspectives in this thesis. In particular, first we provide methods to analyze stability of control systems under arbitrary aperiodic sampling directly from recorded data. Previous approaches to this end required model knowledge, which might be challenging to obtain. Second, we propose a prediction-based approach for design of the aperiodic sampling pattern, called rollout ETC. This approach allows for predictability of the required communication resources and a guarantee of improved sample efficiency, which were both difficult to address in existing ETC schemes.
Data Driven Iterative Learning Control For Discrete Time Systems
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Author : Ronghu Chi
language : en
Publisher: Springer Nature
Release Date : 2022-11-15
Data Driven Iterative Learning Control For Discrete Time Systems written by Ronghu Chi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-15 with Technology & Engineering categories.
This book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system’s output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields.
24th European Symposium On Computer Aided Process Engineering
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Author :
language : en
Publisher: Elsevier
Release Date : 2014-06-20
24th European Symposium On Computer Aided Process Engineering written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-20 with Technology & Engineering categories.
The 24th European Symposium on Computer Aided Process Engineering creates an international forum where scientific and industrial contributions of computer-aided techniques are presented with applications in process modeling and simulation, process synthesis and design, operation, and process optimization. The organizers have broadened the boundaries of Process Systems Engineering by inviting contributions at different scales of modeling and demonstrating vertical and horizontal integration. Contributions range from applications at the molecular level to the strategic level of the supply chain and sustainable development. They cover major classical themes, at the same time exploring a new range of applications that address the production of renewable forms of energy, environmental footprints and sustainable use of resources and water.
Data Driven Methods For Dynamic Systems
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Author : Jason Bramburger
language : en
Publisher: SIAM
Release Date : 2024-11-05
Data Driven Methods For Dynamic Systems written by Jason Bramburger and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-05 with Science categories.
As experimental data sets have grown and computational power has increased, new tools have been developed that have the power to model new systems and fundamentally alter how current systems are analyzed. This book brings together modern computational tools to provide an accurate understanding of dynamic data. The techniques build on pencil-and-paper mathematical techniques that go back decades and sometimes even centuries. The result is an introduction to state-of-the-art methods that complement, rather than replace, traditional analysis of time-dependent systems. Data-Driven Methods for Dynamic Systems provides readers with methods not found in other texts as well as novel ones developed just for this book; an example-driven presentation that provides background material and descriptions of methods without getting bogged down in technicalities; and examples that demonstrate the applicability of a method and introduce the features and drawbacks of their application. The online supplementary material includes a code repository that can be used to reproduce every example and that can be repurposed to fit a variety of applications not found in the book. This book is intended as an introduction to the field of data-driven methods for graduate students. It will also be of interest to researchers who want to familiarize themselves with the discipline. It can be used in courses on dynamical systems, differential equations, and data science.
Data Driven Strategies
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Author : Wang Jianhong
language : en
Publisher: CRC Press
Release Date : 2023-03-31
Data Driven Strategies written by Wang Jianhong and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-31 with Computers categories.
A key challenge in science and engineering is to provide a quantitative description of the systems under investigation, leveraging the noisy data collected. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in engineering. The book aims to develop model-free system analysis and control strategies, i.e., data-driven control from theoretical analysis and engineering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book reviews the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strategies to be considered data-driven control strategies, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strategies are applied to interested engineering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems.
Performance Driven Surrogate Modeling Of High Frequency Structures
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Author : Slawomir Koziel
language : en
Publisher: Springer Nature
Release Date : 2020-02-19
Performance Driven Surrogate Modeling Of High Frequency Structures written by Slawomir Koziel and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-19 with Technology & Engineering categories.
This book discusses surrogate modeling of high-frequency structures including antenna and microwave components. The focus is on constrained or performance-driven surrogates. The presented techniques aim at addressing the limitations of conventional modeling methods, pertinent to the issues of dimensionality and parameter ranges that need to be covered by the surrogate to ensure its design utility. Within performance-driven methodologies, mitigation of these problems is achieved through appropriate confinement of the model domain, focused on the regions promising from the point of view of the relevant design objectives. This enables the construction of reliable surrogates at a fraction of cost required by conventional methods, and to accomplish the modeling tasks where other techniques routinely fail. The book provides a broad selection of specific frameworks, extensively illustrated using examples of real-world microwave and antenna structures along with numerous design examples. Furthermore, the book contains introductory material on data-driven and physics-based surrogates. The book will be useful for the readers working in the area of high-frequency electronics, including microwave engineering, antenna design, microwave photonics, magnetism, especially those that utilize electromagnetic (EM) simulation models in their daily routines. Covers performance-driven and constrained modeling methods, not available in other books to date; Discusses of a wide range of practical case studies including a variety of microwave and antenna structures; Includes design applications of the presented modeling frameworks, including single- and multi-objective parametric optimization.
Adaptive Approximation Based Control
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Author : Jay A. Farrell
language : en
Publisher: John Wiley & Sons
Release Date : 2006-04-14
Adaptive Approximation Based Control written by Jay A. Farrell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-14 with Science categories.
A highly accessible and unified approach to the design and analysis of intelligent control systems Adaptive Approximation Based Control is a tool every control designer should have in his or her control toolbox. Mixing approximation theory, parameter estimation, and feedback control, this book presents a unified approach designed to enable readers to apply adaptive approximation based control to existing systems, and, more importantly, to gain enough intuition and understanding to manipulate and combine it with other control tools for applications that have not been encountered before. The authors provide readers with a thought-provoking framework for rigorously considering such questions as: * What properties should the function approximator have? * Are certain families of approximators superior to others? * Can the stability and the convergence of the approximator parameters be guaranteed? * Can control systems be designed to be robust in the face of noise, disturbances, and unmodeled effects? * Can this approach handle significant changes in the dynamics due to such disruptions as system failure? * What types of nonlinear dynamic systems are amenable to this approach? * What are the limitations of adaptive approximation based control? Combining theoretical formulation and design techniques with extensive use of simulation examples, this book is a stimulating text for researchers and graduate students and a valuable resource for practicing engineers.
Nonlinear Model Reduction By Moment Matching
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Author : Giordano Scarciotti
language : en
Publisher:
Release Date : 2017
Nonlinear Model Reduction By Moment Matching written by Giordano Scarciotti and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Electronic books categories.
Mathematical models are at the core of modern science and technology. An accurate description of behaviors, systems and processes often requires the use of complex models which are difficult to analyze and control. To facilitate analysis of and design for complex systems, model reduction theory and tools allow determining "simpler" models which preserve some of the features of the underlying complex description. A large variety of techniques, which can be distinguished depending on the features which are preserved in the reduction process, has been proposed to achieve this goal. One such a method is the moment matching approach. This monograph focuses on the problem of model reduction by moment matching for nonlinear systems. The central idea of the method is the preservation, for a prescribed class of inputs and under some technical assumptions, of the steady-state output response of the system to be reduced. We present the moment matching approach from this vantage point, covering the problems of model reduction for nonlinear systems, nonlinear time-delay systems, data-driven model reduction for nonlinear systems and model reduction for "discontinuous" input signals. Throughout the monograph linear systems, with their simple structure and strong properties, are used as a paradigm to facilitate understanding of the theory and provide foundation of the terminology and notation. The text is enriched by several numerical examples, physically motivated examples and with connections to well-established notions and tools, such as the phasor transform.