Iterative Partition-Based Moving-Horizon State Estimation PDF Download
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Author: René Schneider
Publisher:
ISBN: 9783844050998
Category :
Languages : en
Pages :
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Book Description
Author: René Schneider
Publisher:
ISBN: 9783844050998
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Meriem Gharbi
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832554564
Category : Technology & Engineering
Languages : en
Pages : 174
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Book Description
In this thesis, we develop and analyze a novel framework for moving horizon estimation (MHE) of linear and nonlinear constrained discrete-time systems, which we refer to as proximity moving horizon estimation. The conceptual idea of the proposed framework is to employ a stabilizing a priori solution in order to ensure stability of MHE and to combine it with an online convex optimization in order to obtain an improved performance without jeopardizing stability. The goal of this thesis is to provide proximity-based MHE approaches with desirable theoretical properties and for which reliable and numerically efficient algorithms allow the estimator to be applied in real-time applications. In more detail, we present constructive and simple MHE design procedures which are tailored to the considered class of dynamical systems in order to guarantee important properties of the resulting estimation error dynamics. Furthermore, we develop computationally efficient MHE algorithms in which a suboptimal state estimate is computed at each time instant after an arbitrary and limited number of optimization algorithm iterations. In particular, we introduce a novel class of anytime MHE algorithms which ensure desirable stability and performance properties of the estimator for any number of optimization algorithm iterations, including the case of a single iteration per time instant. In addition to the obtained theoretical results, we discuss the tuning of the performance criteria in proximity MHE given prior knowledge on the system disturbances and illustrate the theoretical properties and practical benefits of the proposed approaches with various numerical examples from the literature.
Author: Rita Maria de Brito Alves
Publisher: Elsevier
ISBN: 0444534350
Category : Science
Languages : en
Pages : 1229
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Book Description
The 10th International Symposium on Process Systems Engineering, PSE'09, will be held in Salvador-Bahia, Brazil on August 16-20, 2009. The special focus of PSE 2009 is Sustainability, Energy and Engineering. PSE 2009 is the tenth in the triennial series of international symposia on process systems engineering initiated in 1982. The meeting is brings together the worldwide PSE community of researchers and practitioners who are involved in the creation and application of computing-based methodologies for planning, design, operation, control and maintenance of chemical and petrochemical process industries. PSE'09 will look at how the PSE methods and tools can support sustainable resource systems and emerging technologies in the areas of green engineering: environmentally conscious design of industrial processes. PSE methods and tools support: - sustainable resource systems - emerging technologies in the areas of green engineering - environmentally conscious design of industrial processes
Author: Rita Maria de Brito Alves
Publisher: Elsevier
ISBN: 0444534733
Category : Technology & Engineering
Languages : en
Pages : 1230
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Book Description
This book contains the proceedings of the 10e of a series of international symposia on process systems engineering (PSE) initiated in 1982. The special focus of PSE09 is how PSE methods can support sustainable resource systems and emerging technologies in the areas of green engineering. * Contains fully searchable CD of all printed contributions * Focus on sustainable green engineering * 9 Plenary papers, 21 Keynote lectures by leading experts in the field
Author: Masoud Soroush
Publisher: Elsevier
ISBN: 0323996728
Category : Technology & Engineering
Languages : en
Pages : 374
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Book Description
Artificial Intelligence in Manufacturing: Concepts and Methods explains the most successful emerging techniques for applying AI to engineering problems. Artificial intelligence is increasingly being applied to all engineering disciplines, producing more insights into how we understand the world and allowing us to create products in new ways. This book unlocks the advantages of this technology for manufacturing by drawing on work by leading researchers who have successfully developed methods that can apply to a range of engineering applications. The book addresses educational challenges needed for widespread implementation of AI and also provides detailed technical instructions for the implementation of AI methods. Drawing on research in computer science, physics and a range of engineering disciplines, this book tackles the interdisciplinary challenges of the subject to introduce new thinking to important manufacturing problems. - Presents AI concepts from the computer science field using language and examples designed to inspire engineering graduates - Provides worked examples throughout to help readers fully engage with the methods described - Includes concepts that are supported by definitions for key terms and chapter summaries
Author: Yousef Saad
Publisher: SIAM
ISBN: 0898715342
Category : Mathematics
Languages : en
Pages : 537
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Book Description
Mathematics of Computing -- General.
Author: Dan Simon
Publisher: John Wiley & Sons
ISBN: 0470045337
Category : Technology & Engineering
Languages : en
Pages : 554
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Book Description
A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.
Author: Dimitri Bertsekas
Publisher: Athena Scientific
ISBN: 1886529078
Category : Computers
Languages : en
Pages : 498
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Book Description
The purpose of this book is to develop in greater depth some of the methods from the author's Reinforcement Learning and Optimal Control recently published textbook (Athena Scientific, 2019). In particular, we present new research, relating to systems involving multiple agents, partitioned architectures, and distributed asynchronous computation. We pay special attention to the contexts of dynamic programming/policy iteration and control theory/model predictive control. We also discuss in some detail the application of the methodology to challenging discrete/combinatorial optimization problems, such as routing, scheduling, assignment, and mixed integer programming, including the use of neural network approximations within these contexts. The book focuses on the fundamental idea of policy iteration, i.e., start from some policy, and successively generate one or more improved policies. If just one improved policy is generated, this is called rollout, which, based on broad and consistent computational experience, appears to be one of the most versatile and reliable of all reinforcement learning methods. In this book, rollout algorithms are developed for both discrete deterministic and stochastic DP problems, and the development of distributed implementations in both multiagent and multiprocessor settings, aiming to take advantage of parallelism. Approximate policy iteration is more ambitious than rollout, but it is a strictly off-line method, and it is generally far more computationally intensive. This motivates the use of parallel and distributed computation. One of the purposes of the monograph is to discuss distributed (possibly asynchronous) methods that relate to rollout and policy iteration, both in the context of an exact and an approximate implementation involving neural networks or other approximation architectures. Much of the new research is inspired by the remarkable AlphaZero chess program, where policy iteration, value and policy networks, approximate lookahead minimization, and parallel computation all play an important role.
Author: Paul M. Frank
Publisher: Springer Science & Business Media
ISBN: 1447108531
Category : Technology & Engineering
Languages : en
Pages : 449
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Book Description
Advances in Control contains keynote contributions and tutorial material from the fifth European Control Conference, held in Germany in September 1999. The topics covered are of particular relevance to all academics and practitioners in the field of modern control engineering. These include: - Modern Control Theory - Fault Tolerant Control Systems - Linear Descriptor Systems - Generic Robust Control Design - Verification of Hybrid Systems - New Industrial Perspectives - Nonlinear System Identification - Multi-Modal Telepresence Systems - Advanced Strategies for Process Control - Nonlinear Predictive Control - Logic Controllers of Continuous Plants - Two-dimensional Linear Systems. This important collection of work is introduced by Professor P.M. Frank who has almost forty years of experience in the field of automatic control. State-of-the-art research, expert opinions and future developments in control theory and its industrial applications, combine to make this an essential volume for all those involved in control engineering.
Author: Vassilios Petridis
Publisher: Springer Science & Business Media
ISBN: 9780792382904
Category : Science
Languages : en
Pages : 336
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Book Description
The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several "subnetworks" (modules), which may perform the same or re lated tasks, and then use an "appropriate" method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of "lumped" or "monolithic" networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network.
