000			03985nam a22004575c 4500
988			_aSpringer_Robotics_2019
999			_c115432 _d115432 _x1
001			115432
003			ES-MaUEC
005			20230110040234.0
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
008			190611s2019 gw a o |||| 0|eng d
020			_a9783030198824
024	7		_a10.1007/978-3-030-19882-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTJ217 _b2019 EB
100	1		_aQi, Ruiyun _eautor _9671746
245	1	0	_aFuzzy system identification and adaptive control _cby Ruiyun Qi, Gang Tao, Bin Jiang
250			_aFirst edition
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XVII, 282 páginas) _b63 ilustraciones, 56 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aCommunications and Control Engineering _x0178-5354
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aIntroduction -- T-S Fuzzy Systems -- Adaptive Control -- T-S Fuzzy System Identification -- Adaptive T-S Fuzzy State Tracking Control Using State Feedback -- Adaptive T-S Fuzzy Output Tracking Control Using State Feedback -- Adaptive T-S Fuzzy Control Using Output Feedback: SISO Case -- Adaptive T-S Fuzzy Control Using Output Feedback: MIMO Case -- Adaptive T-S Fuzzy Control with Unknown Membership Functions -- Adaptive T-S Fuzzy Control Systems For Fault Compensation -- Conclusions.
520	3		_aThis book provides readers with a systematic and unified framework for identification and adaptive control of Takagi-Sugeno (T-S) fuzzy systems. Its design techniques help readers applying these powerful tools to solve challenging nonlinear control problems. The book embodies a systematic study of fuzzy system identification and control problems, using T-S fuzzy system tools for both function approximation and feedback control of nonlinear systems. Alongside this framework, the book also: introduces basic concepts of fuzzy sets, logic and inference system; discusses important properties of T-S fuzzy systems; develops offline and online identification algorithms for T-S fuzzy systems; investigates the various controller structures and corresponding design conditions for adaptive control of continuous-time T-S fuzzy systems; develops adaptive control algorithms for discrete-time input-output form T-S fuzzy systems with much relaxed design conditions, and discrete-time state-space T-S fuzzy systems; and designs stable parameter-adaptation algorithms for both linearly and nonlinearly parameterized T-S fuzzy systems. The authors address adaptive fault compensation problems for T-S fuzzy systems subject to actuator faults. They cover a broad spectrum of related technical topics and to develop a substantial set of adaptive nonlinear system control tools. Fuzzy System Identification and Adaptive Control helps engineers in the mechanical, electrical and aerospace fields, to solve complex control design problems. The book can be used as a reference for researchers and academics in nonlinear, intelligent, adaptive and fault-tolerant control.
650		7	_2embne _aSistemas difusos _9152595
650		7	_2embne _aAnálisis de sistemas _9138446
650		7	_2embne _aControl automático _9405125
700	1		_aTao, Gang _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aJiang, Bin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030198817
776	0	8	_iPrinted edition: _z9783030198831
776	0	8	_iPrinted edition: _z9783030198848
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-19882-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b12/2019 _eel _zSI