Nonlinear Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nonlinear Systems write by Dongbin Lee. This book was released on 2016-10-19. Nonlinear Systems available in PDF, EPUB and Kindle. The book consists mainly of two parts: Chapter 1 - Chapter 7 and Chapter 8 - Chapter 14. Chapter 1 and Chapter 2 treat design techniques based on linearization of nonlinear systems. An analysis of nonlinear system over quantum mechanics is discussed in Chapter 3. Chapter 4 to Chapter 7 are estimation methods using Kalman filtering while solving nonlinear control systems using iterative approach. Optimal approaches are discussed in Chapter 8 with retarded control of nonlinear system in singular situation, and Chapter 9 extends optimal theory to H-infinity control for a nonlinear control system.Chapters 10 and 11 present the control of nonlinear dynamic systems, twin-rotor helicopter and 3D crane system, which are both underactuated, cascaded dynamic systems. Chapter 12 applies controls to antisynchronization/synchronization in the chaotic models based on Lyapunov exponent theorem, and Chapter 13 discusses developed stability analytic approaches in terms of Lyapunov stability. The analysis of economic activities, especially the relationship between stock return and economic growth, is presented in Chapter 14.
Smoothing Solution for Discrete-Time Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences
Smoothing Solution for Discrete-Time Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Smoothing Solution for Discrete-Time Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences write by Sie Long Kek. This book was released on 2016. Smoothing Solution for Discrete-Time Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences available in PDF, EPUB and Kindle. In this chapter, the performance of the integrated optimal control and parameter estimation (IOCPE) algorithm is improved using a modified fixed-interval smoothing scheme in order to solve the discrete-time nonlinear stochastic optimal control problem. In our approach, a linear model-based optimal control problem with adding the adjustable parameters into the model used is solved iteratively. The aim is to obtain the optimal solution of the original optimal control problem. In the presence of the random noise sequences in process plant and measurement channel, the state dynamics, which is estimated using Kalman filtering theory, is smoothed in a fixed interval. With such smoothed state estimate sequence that reduces the output residual, the feedback optimal control law is then designed. During the computation procedure, the optimal solution of the modified model-based optimal control problem can be updated at each iteration step. When convergence is achieved, the iterative solution approaches to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. Moreover, the convergence of the resulting algorithm is also given. For illustration, optimal control of a continuous stirred-tank reactor problem is studied and the result obtained shows the efficiency of the approach proposed.
Optimal State Estimation
Optimal State Estimation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Optimal State Estimation write by Dan Simon. This book was released on 2006-06-19. Optimal State Estimation available in PDF, EPUB and Kindle. 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.
International Aerospace Abstracts
International Aerospace Abstracts - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook International Aerospace Abstracts write by . This book was released on 1996. International Aerospace Abstracts available in PDF, EPUB and Kindle.
Sampled-Data Models for Linear and Nonlinear Systems
Sampled-Data Models for Linear and Nonlinear Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Sampled-Data Models for Linear and Nonlinear Systems write by Juan I. Yuz. This book was released on 2013-10-17. Sampled-Data Models for Linear and Nonlinear Systems available in PDF, EPUB and Kindle. Sampled-data Models for Linear and Nonlinear Systems provides a fresh new look at a subject with which many researchers may think themselves familiar. Rather than emphasising the differences between sampled-data and continuous-time systems, the authors proceed from the premise that, with modern sampling rates being as high as they are, it is becoming more appropriate to emphasise connections and similarities. The text is driven by three motives: · the ubiquity of computers in modern control and signal-processing equipment means that sampling of systems that really evolve continuously is unavoidable; · although superficially straightforward, sampling can easily produce erroneous results when not treated properly; and · the need for a thorough understanding of many aspects of sampling among researchers and engineers dealing with applications to which they are central. The authors tackle many misconceptions which, although appearing reasonable at first sight, are in fact either partially or completely erroneous. They also deal with linear and nonlinear, deterministic and stochastic cases. The impact of the ideas presented on several standard problems in signals and systems is illustrated using a number of applications. Academic researchers and graduate students in systems, control and signal processing will find the ideas presented in Sampled-data Models for Linear and Nonlinear Systems to be a useful manual for dealing with sampled-data systems, clearing away mistaken ideas and bringing the subject thoroughly up to date. Researchers in statistics and economics will also derive benefit from the reworking of ideas relating a model derived from data sampling to an original continuous system.
