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N. Djelal, N. Saadia, A. Ramdane-Cherif (2019)
Adaptive Force-Vision Control of Robot Manipulator Using Sliding Mode and Fuzzy LogicAutomatic Control and Computer Sciences, 53
S. Arimoto, S. Kawamura, F. Miyazaki (1984)
Bettering operation of Robots by learningJ. Field Robotics, 1
A. Hyo (2012)
Convergence analysis of fractional-order iterative learning controlControl theory & applications
(2013)
Position control method for uncertainty manipulators based on implicit Lyapunov function
(2008)
Design and MATLAB Simulation of Robot Control System, Beijing
Kejun Zhang, Guohua Peng (2017)
Convergence analysis of PIβDα-type fractional-order iterative learning control in the sense of Lp norm2017 36th Chinese Control Conference (CCC)
Iman Ghasemi, A. Noei, J. Sadati (2018)
Sliding mode based fractional-order iterative learning control for a nonlinear robot manipulator with bounded disturbanceTransactions of the Institute of Measurement and Control, 40
(2018)
Research on configurable modular industrial robot motion control system
Xiangchao Cai (2018)
760Mech. Sci. Technol., 37
(2019)
Research on Identification and Control Method of Fractional Iterative Learning, Shandong
(2018)
Application Research of Multi-Motor Synchronous Control Strategy in Multi-Leaf Collimator MultiBlade Control, Lanzhou
(2019)
Research on Iterative Learning Control Algorithms for Several Generalized Systems, Guangdong
(2019)
Robust adaptive iterative learning control for CD player robot arm trajectory tracking
(1950)
Robust fuzzy adaptive compensation control of reconfigurable manipulator
Zijian Luo, Jin Wang (2017)
Study on iterative learning control for Riemann-Liouville type fractional-order systemsDifferential Equations and Applications
Wan Xu, Jie Hou, Wei Yang, Cong Wang (2023)
A double-iterative learning and cross-coupling control design for high-precision motion controlArchives of Electrical Engineering
(2018)
Research on stability control of f light manipulator based on sliding mode PID
H. Delavari, D. Baleanu, J. Sadati (2011)
Stability analysis of Caputo fractional-order nonlinear systems revisitedNonlinear Dynamics, 67
In order to improve the control accuracy between the joints of the manipulator for non-linear uncertain systems such as single-joint manipulators. The \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${{{\text{D}}}^{\alpha }}$$\end{document} type and \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{P}}{{{\text{D}}}^{\alpha }}$$\end{document} type fractional order iterative learning control (ILC) strategies are proposed based on combining fractional order calculus and ILC. The experimental verification was carried out with the two-joint manipulator as the research object. The experimental results are as follows: the fractional order ILC is compared with the traditional ILC, in which the minimum value of the position error of the \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${{{\text{D}}}^{\alpha }}$$\end{document} type fractional order ILC is reduced by 0.00016 and 0.00111 rad compared with the traditional \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{D}}$$\end{document} type ILC, and the \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{P}}{{{\text{D}}}^{\alpha }}$$\end{document} type fractional order ILC compared with the traditional \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\text{PD}}$$\end{document} type ILC, the minimum value of the position error is reduced by 0.00022 and 0.00024 rad respectively. It shows that the algorithm proposed in this paper can improve the tracking performance of the system and improve system control accuracy.
Automatic Control and Computer Sciences – Springer Journals
Published: Jul 1, 2021
Keywords: manipulator; fractional order calculus; ILC; position error
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