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001481180 001__ 1481180
001481180 003__ OCoLC
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001481180 019__ $$a1401056304
001481180 020__ $$a9789819957668$$q(electronic bk.)
001481180 020__ $$a9819957664$$q(electronic bk.)
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001481180 0247_ $$a10.1007/978-981-99-5766-8$$2doi
001481180 035__ $$aSP(OCoLC)1400014212
001481180 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dEBLCP
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001481180 050_4 $$aTJ211.35
001481180 08204 $$a629.8/92$$223/eng/20231012
001481180 1001_ $$aLuo, Xin.
001481180 24510 $$aRobot control and calibration :$$binnovative control schemes and calibration /$$cXin Luo, Zhibin Li, Long Jin, Shuai Li.
001481180 260__ $$aSingapore :$$bSpringer,$$c2023.
001481180 300__ $$a1 online resource
001481180 4901_ $$aSpringerBriefs in Computer Science
001481180 504__ $$aIncludes bibliographical references.
001481180 5050_ $$aIntro -- Preface -- Acknowledgments -- Contents -- Chapter 1: Introduction -- 1.1 Overview -- 1.2 Preliminaries -- 1.2.1 Kinematic Control Problem of the Robot -- 1.2.2 Robot Kinematic Calibration -- 1.3 Book Organization -- References -- Chapter 2: A Novel Model Predictive Control Scheme Based on an Improved Newton Algorithm -- 2.1 Overview -- 2.2 QP Problem -- 2.2.1 Model Predictive Control Scheme -- 2.2.2 ESEN Model Construction -- 2.3 Theoretical Verifications for ESEN Algorithm -- 2.3.1 Preconditions -- 2.3.2 Convergence Analysis -- 2.4 Simulations Based on MPC Scheme
001481180 5058_ $$a2.4.1 Without Extraneous Disturbance -- 2.4.2 With Extraneous Disturbance -- 2.5 Conclusion -- References -- Chapter 3: A Novel Recurrent Neural Network for Robot Control -- 3.1 Overview -- 3.2 Time-Varying Description -- 3.2.1 Problem Formulation -- 3.2.2 RNN Model -- 3.3 Theoretical Analysis of RNN Model -- 3.4 Experiments for RNN Model -- 3.4.1 Simulations -- 3.4.2 The Applications of Robot -- 3.5 Conclusions -- References -- Chapter 4: A Projected Zeroing Neural Network Model for the Motion Generation and Control -- 4.1 Overview -- 4.2 Feedback-Considered Scheme -- 4.3 Neural Network Design
001481180 5058_ $$a4.3.1 Neural Network Design -- 4.3.2 Theoretical Analysis without Noise -- 4.3.3 Theoretical Analysis in Constant-Noise Condition -- 4.3.4 Theoretical Analysis in Bounded Random-Noise Condition -- 4.4 Experimental Validations for the Developed PZNN Model -- 4.4.1 Simulations -- 4.4.2 Experiments for a Kinova JACO2 Robot -- 4.5 Conclusions -- References -- Chapter 5: A Regularization Ensemble Based on Levenberg-Marquardt Algorithm for Robot Calibration -- 5.1 Overview -- 5.2 Diversified Regularized LM Algorithm -- 5.2.1 Regularized Robot Kinematic Error Model -- LM Algorithm
001481180 5058_ $$aL1-Regularized LM Algorithm -- L2-Regularized LM Algorithm -- Elastic Net-Regularized LM Algorithm -- Dropout-Regularized LM Algorithm -- Log-Regularized LM Algorithm -- Swish-Regularized LM Algorithm -- 5.2.2 Ensemble -- 5.3 Experimental Results Based on the Proposed Ensemble -- 5.3.1 General Settings -- Evaluation Metrics -- Dataset -- Experimental Device -- Experimental Process -- 5.3.2 Experimental Calibration Performance for M1-6 -- 5.3.3 Experimental Calibration Performance for Compared Algorithms -- 5.4 Conclusions -- References
001481180 5058_ $$aChapter 6: Novel Evolutionary Computing Algorithms for Robot Calibration -- 6.1 Overview -- 6.2 EKF-ICMA-ES Algorithm -- 6.2.1 Extended Kalman Filter (EKF) -- 6.2.2 Improved Covariance Matrix Adaptive Evolution Strategy (ICMA-ES) -- 6.2.3 Quadratic Interpolated Beetle Antennae Search (QIBAS) -- 6.3 Experimental Results for EKF-ICMA-ES and EKF-QIBAS -- 6.3.1 General Settings -- Evaluation Metrics -- Dataset -- 6.3.2 Experimental Performance -- Experimental Performance for EKF-ICMA-ES -- Experimental Performance for EKF-QIBAS -- 6.4 Conclusions -- References
001481180 506__ $$aAccess limited to authorized users.
001481180 520__ $$aThis book mainly shows readers how to calibrate and control robots. In this regard, it proposes three control schemes: an error-summation enhanced Newton algorithm for model predictive control; RNN for solving perturbed time-varying underdetermined linear systems; and a new joint-drift-free scheme aided with projected ZNN, which can effectively improve robot control accuracy. Moreover, the book develops four advanced algorithms for robot calibration Levenberg-Marquarelt with diversified regularizations; improved covariance matrix adaptive evolution strategy; quadratic interpolated beetle antennae search algorithm; and a novel variable step-size Levenberg-Marquardt algorithm which can effectively enhance robot positioning accuracy. In addition, it is exceedingly difficult for experts in other fields to conduct robot arm calibration studies without calibration data. Thus, this book provides a publicly available dataset to assist researchers from other fields in conducting calibration experiments and validating their ideas. The book also discusses six regularization schemes based on its robot error models, i.e., L1, L2, dropout, elastic, log, and swish. Robots positioning accuracy is significantly improved after calibration. Using the control and calibration methods developed here, readers will be ready to conduct their own research and experiments.
001481180 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 12, 2023).
001481180 650_0 $$aRobots$$xControl systems.$$0(DLC)sh 85004921
001481180 650_0 $$aRobots$$xCalibration.$$0(DLC)sh 85004921
001481180 655_0 $$aElectronic books.
001481180 7001_ $$aLi, Zhibin.
001481180 7001_ $$aJin, Long,$$d1988-
001481180 7001_ $$aLi, Shuai,$$d1983-
001481180 77608 $$iPrint version: $$z9819957656$$z9789819957651$$w(OCoLC)1390677476
001481180 830_0 $$aSpringerBriefs in computer science.
001481180 852__ $$bebk
001481180 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-5766-8$$zOnline Access$$91397441.1
001481180 909CO $$ooai:library.usi.edu:1481180$$pGLOBAL_SET
001481180 980__ $$aBIB
001481180 980__ $$aEBOOK
001481180 982__ $$aEbook
001481180 983__ $$aOnline
001481180 994__ $$a92$$bISE