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008			230925s2023 si | fo |||| 0|eng d
020			_a9789819957668
024	7		_a10.1007/978-981-99-5766-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTJ211 _b2023 EB
100	1		_aLuo, Xin _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689931 _c(profesor)
245	1	0	_aRobot Control and Calibration : _bInnovative Control Schemes and Calibration Algorithms _cby Xin Luo, Zhibin Li, Long Jin, Shuai Li
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_aChapter 1. Introduction -- Chapter 2. A Novel Model Predictive Control Scheme Based on an Improved Newton Algorithm -- Chapter 3. A Novel Recurrent Neural Network for Robot Control -- Chapter 4. A Projected Zeroing Neural Network Model for the Motion Generation and Control -- Chapter 5. A Regularization Ensemble Based on Levenberg-Marquardt Algorithm for Robot Calibration -- Chapter 6. Novel Evolutionary Computing Algorithms for Robot Calibration -- Chapter 7. A Highly Accurate Calibrator Based on a Novel Variable Step-Size Levenberg-Marquardt Algorithm -- Chapter 8. Conclusion and Future Work.
520			_aThis book mainly shows readers how to calibrate and control robots. In this regard, it proposes three control schemes: an error-summation enhanced Newton algorithm for model predictive control; RNN for solving perturbed time-varying underdetermined linear systems; and a new joint-drift-free scheme aided with projected ZNN, which can effectively improve robot control accuracy. Moreover, the book develops four advanced algorithms for robot calibration - Levenberg-Marquarelt with diversified regularizations; improved covariance matrix adaptive evolution strategy; quadratic interpolated beetle antennae search algorithm; and a novel variable step-size Levenberg-Marquardt algorithm - which can effectively enhance robot positioning accuracy. In addition, it is exceedingly difficult for experts in other fields to conduct robot arm calibration studies without calibration data. Thus, this book provides a publicly available dataset to assist researchers from other fields in conducting calibration experiments and validating their ideas. The book also discusses six regularization schemes based on its robot error models, i.e., L1, L2, dropout, elastic, log, and swish. Robots' positioning accuracy is significantly improved after calibration. Using the control and calibration methods developed here, readers will be ready to conduct their own research and experiments.
988			_aSpringer_Computer_2023
650		7	_2embne _9160676 _aRobótica
700	1		_9689932 _aLi, Zhibin _eautor
700	1		_9689933 _aJin, Long _eautor _d1988-
700	1		_9675026 _aLi, Shuai _eautor _d1982-
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-5766-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _ean _zSI