A New Fuzzy Iterative Learning Control Algorithm for Single Joint Manipulator

• Autorzy: Wang M. , Bian G. , Li H.

Identyfikatory

DOI

10.1515/acsc-2016-0017

• Języki publikacji: EN

Abstrakty

EN

This paper present a new fuzzy iterative learning control design to solve the trajectory tracking problem and performing repetitive tasks for rigid robot manipulators. Several times’ iterations are needed to make the system tracking error converge, especially in the first iteration without experience. In order to solve that problem, fuzzy control and iterative learning control are combined, where fuzzy control is used to tracking trajectory at the first learning period, and the output of fuzzy control is recorded as the initial control inputs of ILC. The new algorithm also adopts gain self-tuning by fuzzy control, in order to improve the convergence rate. Simulations illustrate the effectiveness and convergence of the new algorithm and advantages compared to traditional method.

Słowa kluczowe

EN

iterative learning control; fuzzy control; fuzzy gain adjustment; single joint manipulator

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2016
• Tom: Vol. 26, no. 3
• Strony: 297--310
• Opis fizyczny: Bibliogr. 13 poz., rys., schem., tab., wykr., wzory

Twórcy

autor

Wang M.

• School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan, Hubei, P.R. China Postcode: 430070

autor

Bian G.

• School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan, Hubei, P.R. China Postcode: 430070

autor

Li H.

• School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan, Hubei, P.R. China Postcode: 430070

Bibliografia

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• [9] Y. Fang, Y. C. Soh and G. G. Feng: Convergence analysis of iterative learning control with uncertain initial conditions. Proc. of the 4th World Congress on Intelligent Control and Automation, Shanghai, China, (2002), 960-963.
• [10] X. G. Zhang and H. Lin: Robustness analysis of open-closed-loop D-type iterative learning control algorithm for nonlinear systems with deviations on initial state. IMACS Multiconference on Computational Engineering in Systems Applications, Beijing, China, (2006), 1707-1711.
• [11] S. Chekkal, N. A. Lahacani, D. Aouzellak and K. Ghedamsi: Fuzzy logic control strategy of wind generator based on the dual-stator induction generator. Electrical Power and Energy Systems, 59 (2014), 166-175.
• [12] A. Boulkroune, N. Bounar, M. M. Saad and M. Farza: Indirect adaptive fuzzy control scheme based on observer for nonlinear systems: A novel SPR-filter approach. Neurocomputing, 135 (2014), 378-387.
• [13] M. T. Cao, J. Neto and H. L. Huy: Fuzzy logic based controller for induction motor drives. Proc. of the Canadian Conf. on Electrical and Computer Engineering, 2 (1996), 631-634.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę