ROBOTICA & MANAGEMENT - Vol. 24, No. 2, December 2019, pp. 11-15

Learning Algorithm in Trajectory Tracking Control by Anfis and Neuron Network for Rotary Inverted Pendulum

Thi-Thanh-Hoang Le *, Gia-Bao Hong *, Minh-Tam Nguyen *, Van-Khanh Doan **, Thien-Van Nguyen ***, Le-Long-Thien Ho *

* Ho Chi Minh City University of Technology and Education (HCMUTE)
01-Vo Van Ngan street, Thu Duc district, Ho Chi Minh City, Vietnam

** “Politehnica” University of Bucharest (UPB)
Splaiul Independenţei nr. 313, Sector 6, Bucharest, Romania

*** Academy of Science and Technology
No-17, Hoang Sam Street, Nghia Do ward, Cau Giay district, Ha noi, Vietnam

Abstract: Matlab is a useful tool in many fields of science and technology, especially control theory. In this paper, toolkits of Matlab (Anfis and Neuron Network Training tool) are used to learn the operation of Incremental Sliding Mode Control algorithm in trajectory tracking control for Rotary Inverted Pendulum. The data learning is collected from experiment and verified through real-time control. The results in this paper show the ability of these two toolkits on learning control process. Thence, this method can be extended to apply on other control objects.

Keywords: Anfis, Neuron Network Training, Incremental Sliding Mode Control, trajectory tracking, learning control process.

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[1] Yevgeniy Gershteyn, Larisa Perman, “Matlab: ANFIS Toolbox”, The MathWorks, Apr. 22, 2003.

[2] Jacek Bartman, Zbigniew Gomółka, Bogusław Twaróg, “ANN training – the analysis of the selected procedures in Matlab environment”, Computing in Science and Technology, pp.88-101, May. 2015.

[3] Y. Hao, J. Yi, D. Zhao, W. Wei, “Proposal of incremental sliding mode control”, Proceedings of the First International Conference on Innovative Computing, Information and Control (ICICIC'06), 2008.

[4] Y. Hao, J. Yi, D. Zhao, D. Qian, “Robust control using incremental sliding mode for underactuated systems with mismatched uncertainties “, 2008 American Control Conference Westin Seattle Hotel, Seattle, Washington, USA Jun. 11-13, 2008.

[5] Z. Djamila, B. Khier: “Optimal sliding mode control of the pendubot”, Global Journal of Computer Science and Programming, pp.008-014, Jun. 2014.

[6] Rotary Pendulum Workbook, Quanser Inc, 2011.

[7] N. Walia, H. Singh, A. Sharma, “ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey”, International Journal of Computer Applications (0975 – 8887) Volume 123 – No.13, Aug. 2015.

[8] N. V. Chuc, “Tổng quan về Mạng Neuron (Neural Network)”, Business Intelligent Solution, May. 12, 2016.

[9] H. Jie-Ren, “Balance Control of a Car - Pole Inverted Pendulum System”, National Cheng Kung University library, 2002.