Identificacion Principal del registro INGC-EBK-000937

Control Number Identifier AR-LpUFI

LAST MODIFICATION DATE 20160920110547

CONTROL FIELD cr nn 008mamaa

CONTROL FIELD 140619s2014 si | s |||| 0|eng d

a International Standard Book Number 9789814585606

a Standard number or code 10.1007/978-981-4585-60-6

a Personal name Wang, Danwei.

a Title Practical Iterative Learning Control with Frequency Domain Design and Sampled Data Implementation

h Medium [libro electrónico] /

c Statement of responsibility, etc by Danwei Wang, Yongqiang Ye, Bin Zhang.

a Place of publication, distribution, etc Singapore :

b Name of publisher, distributor, etc Springer Singapore :

b Name of publisher, distributor, etc Imprint: Springer,

c Date of publication, distribution, etc 2014.

a Extent xii, 226 p. :

b Other physical details il.

a Series statement Advances in Industrial Control,

x International Standard Serial Number 1430-9491

a Formatted contents note Introduction -- Extend Learnable Band and Multi-channel Configuration -- Learnable Bandwidth Extension by Auto-Tunings -- Reverse Time Filtering Based ILC -- Wavelet Transform based Frequency Tuning ILC -- Learning Transient Performance with Cutoff-Frequency Phase-In -- Downsampled ILC -- Cyclic Pseudo-Downsampled ILC.

a Summary, etc This book is on the iterative learning control (ILC) with focus on the design and implementation. We approach the ILC design based on the frequency domain analysis and address the ILC implementation based on the sampled data methods. This is the first book of ILC from frequency domain and sampled data methodologies. The frequency domain design methods offer ILC users insights to the convergence performance which is of practical benefits. This book presents a comprehensive framework with various methodologies to ensure the learnable bandwidth in the ILC system to be set with a balance between learning performance and learning stability. The sampled data implementation ensures effective execution of ILC in practical dynamic systems. The presented sampled data ILC methods also ensure the balance of performance and stability of learning process. Furthermore, the presented theories and methodologies are tested with an ILC controlled robotic system. The experimental results show that the machines can work in much higher accuracy than a feedback control alone can offer. With the proposed ILC algorithms, it is possible that machines can work to their hardware design limits set by sensors and actuators. The target audience for this book includes scientists, engineers and practitioners involved in any systems with repetitive operations.

a Topical term or geographic name as entry element Artificial intelligence.

a Topical term or geographic name as entry element Neural networks (Computer science).

a Topical term or geographic name as entry element Statistical physics.

a Topical term or geographic name as entry element Computational intelligence.

a Topical term or geographic name as entry element Engineering.

a Topical term or geographic name as entry element Mathematical Models of Cognitive Processes

a Topical term or geographic name as entry element Nonlinear Dynamics.

a Personal name Ye, Yongqiang.

a Personal name Zhang, Bin.

u Uniform Resource Identifier (R) http://dx.doi.org/10.1007/978-981-4585-60-6

c item type EBK

a descripción COM