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Issue title: Some highlights on fuzzy systems and data mining
Guest editors: Shilei Sun, Silviu Ionita, Eva Volná, Andrey Gavrilov and Feng Liu
Article type: Research Article
Authors: Wang, Xiaokana; c; * | Dong, Haironga; * | Sun, Xunbinb | Yao, Xiumingb
Affiliations: [a] State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China | [b] School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China | [c] Henan Mechanical and Electrical Vocational College, Xinzheng, China
Correspondence: [*] Corresponding author. Xiaokan Wang and Hairong Dong, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China. Tel.: +86 13633806151; Fax: +86 0371 85901017; E-mails: wxkbbg@163.com (X. Wang); hrdong@bjtu.edu.cn (H. Dong).
Abstract: As a typical, nonlinear system with instability, high order, strong coupling and multiple variations, the inverted pendulum model is the focus and research object of many experts and scholars in the control field. We propose the PD control method of the system based on the adaptive fuzzy compensation in order to weaken the impact of uncertainty in regards to external disturbances, as well as improve the precise control of the inverted pendulum system when the parameters are unknown. It uses the fuzzy method to conduct fuzzy approximation on the nonlinear system of the inverted pendulum by modeling the nonlinear inverted pendulum system to weaken the impact of uncertainty and achieve complete compensation for the nonlinear system. Then, it establishes the adaptive PD fuzzy controller, forms the adaptive control law, applies the Lyapunov function to verify the stability and robustness of the system and finally achieves the intelligent, optimal control of the system. Simulation results show that the control method can achieve the optimal control of the tracking error and parameter error, has a better anti-interference ability and assures system stability.
Keywords: Inverted pendulum, adaptive fuzzy control, compensation, nonlinear system
DOI: 10.3233/JIFS-169186
Journal: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 6, pp. 3013-3019, 2016
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