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020 _a9783030333843
024 7 _a10.1007/978-3-030-33384-3
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQA402.3
_b2020 EB
100 1 _aZhang, Yinyan
_eautor
_9672543
245 1 0 _aDeep Reinforcement Learning with Guaranteed Performance
_bA Lyapunov-Based Approach
_cby Yinyan Zhang, Shuai Li, Xuefeng Zhou.
250 _a1st ed. 2020.
264 1 _aCham
_bSpringer International Publishing :
_bImprint: Springer
_c2020.
300 _a1 recurso en línea (XVII, 225 páginas)
_b 61 ilustraciones, 50 ilustraciones a color.
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
490 0 _aStudies in Systems Decision and Control
_x2198-4182
_v265
490 0 _aIntelligent Technologies and Robotics (Springer-42732)
505 0 _aA Survey of Near-Optimal Control of Nonlinear Systems -- Near-Optimal Control with Input Saturation -- Adaptive Near-Optimal Control with Full-State Feedback -- Adaptive Near-Optimal Control Using Sliding Mode -- Model-Free Adaptive Near-Optimal Tracking Control -- Adaptive Kinematic Control of Redundant Manipulators -- Redundancy Resolution with Periodic Input Disturbance.
520 3 _aThis book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.
988 _aPrimersemestre_2020_Robotics
650 7 _2embne
_9145606
_aControl, Teoría de
700 1 _aLi, Shuai
_eautor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aZhou, Xuefeng
_eautor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783030333836
776 0 8 _iPrinted edition:
_z9783030333850
776 0 8 _iPrinted edition:
_z9783030333867
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-33384-3
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
_n0
998 _b03/2020
_dz
_ek
_zSI