000			03581nam a22005415i 4500
001			978-981-4585-60-6
003			DE-He213
005			20200421112233.0
007			cr nn 008mamaa
008			140619s2014 si | s |||| 0|eng d
020			_a9789814585606 _9978-981-4585-60-6
024	7		_a10.1007/978-981-4585-60-6 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aWang, Danwei. _eauthor.
245	1	0	_aPractical Iterative Learning Control with Frequency Domain Design and Sampled Data Implementation _h[electronic resource] / _cby Danwei Wang, Yongqiang Ye, Bin Zhang.
264		1	_aSingapore : _bSpringer Singapore : _bImprint: Springer, _c2014.
300			_aXII, 226 p. 162 illus., 120 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvances in Industrial Control, _x1430-9491
505	0		_aIntroduction -- 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.
520			_aThis 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.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aNeural networks (Computer science).
650		0	_aStatistical physics.
650		0	_aComputational intelligence.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aMathematical Models of Cognitive Processes and Neural Networks.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aNonlinear Dynamics.
700	1		_aYe, Yongqiang. _eauthor.
700	1		_aZhang, Bin. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9789814585590
830		0	_aAdvances in Industrial Control, _x1430-9491
856	4	0	_uhttp://dx.doi.org/10.1007/978-981-4585-60-6
912			_aZDB-2-ENG
942			_cEBK
999			_c58093 _d58093