LEADER 03871nam 22007215 450 001 9910484216903321 005 20251113185056.0 010 $a3-030-33384-1 024 7 $a10.1007/978-3-030-33384-3 035 $a(CKB)4100000009836392 035 $a(MiAaPQ)EBC5975970 035 $a(DE-He213)978-3-030-33384-3 035 $a(PPN)243771169 035 $a(EXLCZ)994100000009836392 100 $a20191109d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Reinforcement Learning with Guaranteed Performance $eA Lyapunov-Based Approach /$fby Yinyan Zhang, Shuai Li, Xuefeng Zhou 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (xvii, 225 pages) $cillustrations 225 1 $aStudies in Systems, Decision and Control,$x2198-4190 ;$v265 311 08$a3-030-33383-3 327 $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. 330 $aThis book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks. 410 0$aStudies in Systems, Decision and Control,$x2198-4190 ;$v265 606 $aAutomatic control 606 $aRobotics 606 $aComputational intelligence 606 $aSystem theory 606 $aControl theory 606 $aAutomation 606 $aControl and Systems Theory 606 $aRobotics 606 $aComputational Intelligence 606 $aSystems Theory, Control 606 $aControl, Robotics, Automation 615 0$aAutomatic control. 615 0$aRobotics. 615 0$aComputational intelligence. 615 0$aSystem theory. 615 0$aControl theory. 615 0$aAutomation. 615 14$aControl and Systems Theory. 615 24$aRobotics. 615 24$aComputational Intelligence. 615 24$aSystems Theory, Control. 615 24$aControl, Robotics, Automation. 676 $a629.8312 676 $a629.8312 700 $aZhang$b Yinyan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0946977 702 $aLi$b Shuai$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aZhou$b Xuefeng$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484216903321 996 $aDeep Reinforcement Learning with Guaranteed Performance$92139507 997 $aUNINA