000			04895nam a22005895i 4500
001			978-1-84628-859-3
003			DE-He213
005			20161121231157.0
007			cr nn 008mamaa
008			100301s2007 xxk| s |||| 0|eng d
020			_a9781846288593 _9978-1-84628-859-3
024	7		_a10.1007/978-1-84628-859-3 _2doi
050		4	_aTJ212-225
072		7	_aTJFM _2bicssc
072		7	_aTEC004000 _2bisacsh
082	0	4	_a629.8 _223
100	1		_aAhn, Hyo-Sung. _eauthor.
245	1	0	_aIterative Learning Control _h[electronic resource] : _bRobustness and Monotonic Convergence for Interval Systems / _cby Hyo-Sung Ahn, YangQuan Chen, Kevin L. Moore.
264		1	_aLondon : _bSpringer London, _c2007.
300			_aXVIII, 230 p. 36 illus., 4 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		_aCommunications and Control Engineering, _x0178-5354
505	0		_aIterative Learning Control Overview -- An Overview of the ILC Literature -- The Super-vector Approach -- Robust Interval Iterative Learning Control -- Robust Interval Iterative Learning Control: Analysis -- Schur Stability Radius of Interval Iterative Learning Control -- Iterative Learning Control Design Based on Interval Model Conversion -- Iteration-domain Robustness -- Robust Iterative Learning Control: H? Approach -- Robust Iterative Learning Control: Stochastic Approaches -- Conclusions.
520			_aThis monograph studies the design of robust, monotonically-convergent iterative learning controllers for discrete-time systems. Two key problems with the fundamentals of iterative learning control (ILC) design as treated by existing work are: first, many ILC design strategies assume nominal knowledge of the system to be controlled and; second, it is well-known that many ILC algorithms do not produce monotonic convergence, though in applications monotonic convergence is often essential. Iterative Learning Control takes account of the recently-developed comprehensive approach to robust ILC analysis and design established to handle the situation where the plant model is uncertain. Considering ILC in the iteration domain, it presents a unified analysis and design framework that enables designers to consider both robustness and monotonic convergence for typical uncertainty models, including parametric interval uncertainties, iteration-domain frequency uncertainty, and iteration-domain stochastic uncertainty. Topics include: • Use of a lifting technique to convert the two-dimensional ILC system, which has dynamics in both the time and iteration domains, into the supervector framework, which yields a one-dimensional system, with dynamics only in the iteration domain. • Development of iteration-domain uncertainty models in the supervector framework. • ILC design for monotonic convergence when the plant is subject to parametric interval uncertainty in its Markov matrix. • An algebraic H-infinity design methodology for ILC design when the plant is subject to iteration-domain frequency uncertainty. • Development of Kalman-filter-based ILC algorithms when the plant is subject to iteration-domain stochastic uncertainties. • Analytical determination of the base-line error of ILC algorithms. • Solutions to three fundamental robust interval computational problems (used as basic tools for designing robust ILC controllers): finding the maximum singular value of an interval matrix, determining the robust stability of interval polynomial matrix, and obtaining the power of an interval matrix. Iterative Learning Control will be of great interest to academic researchers in control theory and to industrial control engineers working in robotics-oriented manufacturing and batch-processing-based industries. Graduate students of intelligent control will also find this volume instructive.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aBioinformatics.
650		0	_aSystem theory.
650		0	_aControl engineering.
650		0	_aRobotics.
650		0	_aMechatronics.
650		0	_aBiomedical engineering.
650	1	4	_aEngineering.
650	2	4	_aControl.
650	2	4	_aSystems Theory, Control.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aControl, Robotics, Mechatronics.
650	2	4	_aBioinformatics.
650	2	4	_aBiomedical Engineering.
700	1		_aChen, YangQuan. _eauthor.
700	1		_aMoore, Kevin L. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781846288463
830		0	_aCommunications and Control Engineering, _x0178-5354
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-84628-859-3
912			_aZDB-2-ENG
950			_aEngineering (Springer-11647)
999			_c509682 _d509682