Iterative Learning Control - 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 Iterative Learning Control write by David H. Owens. This book was released on 2015-10-31. Iterative Learning Control available in PDF, EPUB and Kindle. This book develops a coherent and quite general theoretical approach to algorithm design for iterative learning control based on the use of operator representations and quadratic optimization concepts including the related ideas of inverse model control and gradient-based design. Using detailed examples taken from linear, discrete and continuous-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately as their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates the underlying robustness of the paradigm and also includes new control laws that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference and auxiliary signals and also to support new properties such as spectral annihilation. Iterative Learning Control will interest academics and graduate students working in control who will find it a useful reference to the current status of a powerful and increasingly popular method of control. The depth of background theory and links to practical systems will be of use to engineers responsible for precision repetitive processes.
Iterative Learning Control
Iterative Learning Control - 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 Iterative Learning Control write by Zeungnam Bien. This book was released on 2012-12-06. Iterative Learning Control available in PDF, EPUB and Kindle. Iterative Learning Control (ILC) differs from most existing control methods in the sense that, it exploits every possibility to incorporate past control informa tion, such as tracking errors and control input signals, into the construction of the present control action. There are two phases in Iterative Learning Control: first the long term memory components are used to store past control infor mation, then the stored control information is fused in a certain manner so as to ensure that the system meets control specifications such as convergence, robustness, etc. It is worth pointing out that, those control specifications may not be easily satisfied by other control methods as they require more prior knowledge of the process in the stage of the controller design. ILC requires much less information of the system variations to yield the desired dynamic be haviors. Due to its simplicity and effectiveness, ILC has received considerable attention and applications in many areas for the past one and half decades. Most contributions have been focused on developing new ILC algorithms with property analysis. Since 1992, the research in ILC has progressed by leaps and bounds. On one hand, substantial work has been conducted and reported in the core area of developing and analyzing new ILC algorithms. On the other hand, researchers have realized that integration of ILC with other control techniques may give rise to better controllers that exhibit desired performance which is impossible by any individual approach.
Real-time Iterative Learning Control
Real-time Iterative Learning Control - 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 Real-time Iterative Learning Control write by Jian-Xin Xu. This book was released on 2008-12-12. Real-time Iterative Learning Control available in PDF, EPUB and Kindle. Real-time Iterative Learning Control demonstrates how the latest advances in iterative learning control (ILC) can be applied to a number of plants widely encountered in practice. The book gives a systematic introduction to real-time ILC design and source of illustrative case studies for ILC problem solving; the fundamental concepts, schematics, configurations and generic guidelines for ILC design and implementation are enhanced by a well-selected group of representative, simple and easy-to-learn example applications. Key issues in ILC design and implementation in linear and nonlinear plants pervading mechatronics and batch processes are addressed, in particular: ILC design in the continuous- and discrete-time domains; design in the frequency and time domains; design with problem-specific performance objectives including robustness and optimality; design in a modular approach by integration with other control techniques; and design by means of classical tools based on Bode plots and state space.
Iterative Learning Control
Iterative Learning Control - 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 Iterative Learning Control write by Yangquan Chen. This book was released on 2007-10-03. Iterative Learning Control available in PDF, EPUB and Kindle. This book provides readers with a comprehensive coverage of iterative learning control. The book can be used as a text or reference for a course at graduate level and is also suitable for self-study and for industry-oriented courses of continuing education. Ranging from aerodynamic curve identification robotics to functional neuromuscular stimulation, Iterative Learning Control (ILC), started in the early 80s, is found to have wide applications in practice. Generally, a system under control may have uncertainties in its dynamic model and its environment. One attractive point in ILC lies in the utilisation of the system repetitiveness to reduce such uncertainties and in turn to improve the control performance by operating the system repeatedly. This monograph emphasises both theoretical and practical aspects of ILC. It provides some recent developments in ILC convergence and robustness analysis. The book also considers issues in ILC design. Several practical applications are presented to illustrate the effectiveness of ILC. The applied examples provided in this monograph are particularly beneficial to readers who wish to capitalise the system repetitiveness to improve system control performance.
Iterative Learning Control for Deterministic Systems
Iterative Learning Control for Deterministic 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 Iterative Learning Control for Deterministic Systems write by Kevin L. Moore. This book was released on 2012-12-06. Iterative Learning Control for Deterministic Systems available in PDF, EPUB and Kindle. The material presented in this book addresses the analysis and design of learning control systems. It begins with an introduction to the concept of learning control, including a comprehensive literature review. The text follows with a complete and unifying analysis of the learning control problem for linear LTI systems using a system-theoretic approach which offers insight into the nature of the solution of the learning control problem. Additionally, several design methods are given for LTI learning control, incorporating a technique based on parameter estimation and a one-step learning control algorithm for finite-horizon problems. Further chapters focus upon learning control for deterministic nonlinear systems, and a time-varying learning controller is presented which can be applied to a class of nonlinear systems, including the models of typical robotic manipulators. The book concludes with the application of artificial neural networks to the learning control problem. Three specific ways to neural nets for this purpose are discussed, including two methods which use backpropagation training and reinforcement learning. The appendices in the book are particularly useful because they serve as a tutorial on artificial neural networks.
