Iterative Learning Control for Flexible Structures, Meng Tingting, He Wei
Автор: Zeungnam Bien; Jian-Xin Xu Название: Iterative Learning Control ISBN: 1461375754 ISBN-13(EAN): 9781461375753 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Описание: This book presents an in-depth discussion of iterative learning control (ILC) with passive incomplete information, highlighting the incomplete input and output data resulting from practical factors such as data dropout, transmission disorder, communication delay, etc.—a cutting-edge topic in connection with the practical applications of ILC. It describes in detail three data dropout models: the random sequence model, Bernoulli variable model, and Markov chain model—for both linear and nonlinear stochastic systems. Further, it proposes and analyzes two major compensation algorithms for the incomplete data, namely, the intermittent update algorithm and successive update algorithm. Incomplete information environments include random data dropout, random communication delay, random iteration-varying lengths, and other communication constraints. With numerous intuitive figures to make the content more accessible, the book explores several potential solutions to this topic, ensuring that readers are not only introduced to the latest advances in ILC for systems with random factors, but also gain an in-depth understanding of the intrinsic relationship between incomplete information environments and essential tracking performance. It is a valuable resource for academics and engineers, as well as graduate students who are interested in learning about control, data-driven control, networked control systems, and related fields.
Описание: This book is based on the authors` research on the stabilization and fault-tolerant control of batch processes, which are flourishing topics in the field of control system engineering.
Автор: Zeungnam Bien; Jian-Xin Xu Название: Iterative Learning Control ISBN: 0792382137 ISBN-13(EAN): 9780792382133 Издательство: Springer Рейтинг: Цена: 26122.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides a comprehensive overview of iterative learning control theory and techniques relevant to industrial automation, and focuses on research directions for the 21st century. This title examines the important aspects of this technology. It also provides coverage of ILC`s history, its real-world applications, and its robustness and convergence.
Автор: David H. Owens Название: Iterative Learning Control ISBN: 144716928X ISBN-13(EAN): 9781447169284 Издательство: Springer Рейтинг: Цена: 16977.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Iterative Learning Control: Background and Review. Mathematical and Linear Modelling Methodologies.- Norm Optimal Iterative Learning Control: An Optimal Control Perspective.- Predicting the Effects of Non-minimum-phase Zeros.- Predictive Norm Optimal Iterative Learning Control.- Other Applications of Norm Optimal Iterative Learning Control.- Successive Projection Algorithms.- Parameter Optimal Iterative Learning Control.- Robustness of Parameter Optimal Iterative Learning Control.- Multi-parameter Optimal Iterative Learning Control.- No Normal 0 false false false EN-GB X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name: "Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow: yes; mso-style-priority:99; mso-style-parent: ""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination: widow-orphan; font-size:11.0pt; font-family: "Calibri","sans-serif"; mso-ascii-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-hansi-theme-font: minor-latin; mso-fareast-language: EN-US;} nlinear Iterative Learning Control and Optimization.
Описание: This book presents a comprehensive and detailed study on iterative learning control (ILC) for systems with iteration-varying trial lengths. Instead of traditional ILC, which requires systems to repeat on a fixed time interval, this book focuses on a more practical case where the trial length might randomly vary from iteration to iteration. The iteration-varying trial lengths may be different from the desired trial length, which can cause redundancy or dropouts of control information in ILC, making ILC design a challenging problem. The book focuses on the synthesis and analysis of ILC for both linear and nonlinear systems with iteration-varying trial lengths, and proposes various novel techniques to deal with the precise tracking problem under non-repeatable trial lengths, such as moving window, switching system, and searching-based moving average operator. It not only discusses recent advances in ILC for systems with iteration-varying trial lengths, but also includes numerous intuitive figures to allow readers to develop an in-depth understanding of the intrinsic relationship between the incomplete information environment and the essential tracking performance. This book is intended for academic scholars and engineers who are interested in learning about control, data-driven control, networked control systems, and related fields. It is also a useful resource for graduate students in the above field.
Описание: This book presents an in-depth discussion of iterative learning control (ILC) with passive incomplete information, highlighting the incomplete input and output data resulting from practical factors such as data dropout, transmission disorder, communication delay, etc.—a cutting-edge topic in connection with the practical applications of ILC. It describes in detail three data dropout models: the random sequence model, Bernoulli variable model, and Markov chain model—for both linear and nonlinear stochastic systems. Further, it proposes and analyzes two major compensation algorithms for the incomplete data, namely, the intermittent update algorithm and successive update algorithm. Incomplete information environments include random data dropout, random communication delay, random iteration-varying lengths, and other communication constraints. With numerous intuitive figures to make the content more accessible, the book explores several potential solutions to this topic, ensuring that readers are not only introduced to the latest advances in ILC for systems with random factors, but also gain an in-depth understanding of the intrinsic relationship between incomplete information environments and essential tracking performance. It is a valuable resource for academics and engineers, as well as graduate students who are interested in learning about control, data-driven control, networked control systems, and related fields.
Описание: Iterative learning control (ILC) has its origins in the control of processes that perform a task repetitively with a view to improving accuracy from trial to trial by using information from previous executions of the task. This brief shows how a classic application of this technique – trajectory following in robots – can be extended to neurological rehabilitation after stroke. Regaining upper limb movement is an important step in a return to independence after stroke, but the prognosis for such recovery has remained poor. Rehabilitation robotics provides the opportunity for repetitive task-oriented movement practice reflecting the importance of such intense practice demonstrated by conventional therapeutic research and motor learning theory. Until now this technique has not allowed feedback from one practice repetition to influence the next, also implicated as an important factor in therapy. The authors demonstrate how ILC can be used to adjust external functional electrical stimulation of patients’ muscles while they are repeatedly performing a task in response to the known effects of stimulation in previous repetitions. As the motor nerves and muscles of the arm reaquire the ability to convert an intention to move into a motion of accurate trajectory, force and rapidity, initially intense external stimulation can now be scaled back progressively until the fullest possible independence of movement is achieved.
Автор: Alberto Cavallo; Giuseppe de Maria; Ciro Natale; S Название: Active Control of Flexible Structures ISBN: 1447157079 ISBN-13(EAN): 9781447157076 Издательство: Springer Рейтинг: Цена: 16977.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A complete solution for vibration control problems in structures that may be subject to a broadband primary vibration field, this book covers a range of issues from optimal placement of sensors and actuators to controller implementation and rapid prototyping.
Автор: Atul G. Kelkar; Suresh M. Joshi Название: Control of Nonlinear Multibody Flexible Space Structures ISBN: 3540760938 ISBN-13(EAN): 9783540760931 Издательство: Springer Рейтинг: Цена: 12157.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Addressing the difficult problem of controlling flexible spacecraft having multiple articulated appendages is the aim of this volume. These systems are characterised by highly nonlinear dynamics, flexibility in members and joints, low inherent damping, and modeling uncertainty.
Автор: Yangquan Chen; Changyun Wen Название: Iterative Learning Control ISBN: 1852331909 ISBN-13(EAN): 9781852331900 Издательство: Springer Рейтинг: Цена: 14365.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: From aerodynamic curve identification robotics to neuromuscular stimulation, Iterative Learning Control (ILC), has many applications. A system may have uncertainties in its dynamic model and its environment. Using system repetitiveness, ILC reduces uncertainties and improves control performance.
Описание: This book examines iterative learning control (ILC) with a focus on design and implementation. It presents a framework with various methodologies to ensure the learnable bandwidth in the ILC system to be set with a balance between performance and stability.
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