TY - JOUR
T1 - A New Self-Organizing Fuzzy Cerebellar Model Articulation Controller for Uncertain Nonlinear Systems Using Overlapped Gaussian Membership Functions
AU - Huynh, Tuan Tu
AU - Lin, Chih Min
AU - Le, Tien Loc
AU - Cho, Hsing Yueh
AU - Pham, Thanh Thao T.
AU - Le, Nguyen Quoc Khanh
AU - Chao, Fei
N1 - Funding Information:
Manuscript received January 15, 2019; revised July 28, 2019 and October 3, 2019; accepted October 24, 2019. Date of publication November 22, 2019; date of current version July 14, 2020. This work was supported by the Ministry of Science and Technology of the Republic of China under Grant MOST 106-2221-E-155-MY3. (Corresponding authors: Tuan-Tu Huynh; Chih-Min Lin.) T.-T. Huynh and T.-L. Le are with the Department of Electrical Engineering, Yuan Ze University, Taoyuan 320, Taiwan, R.O.C., and also with the Department of Electrical Electronic and Mechanical Engineering, Lac Hong University, Bien Hoa 810000, Vietnam (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 1982-2012 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11
Y1 - 2020/11
N2 - This article proposes an efficient intelligent control structure for uncertain nonlinear systems. This controller is a new self-organizing fuzzy cerebellar model articulation controller (CMAC), which has a framework that includes a CMAC and which uses sliding mode control. A new mixed Gaussian membership function (GMF) is created using a prior GMF and a present GMF for each layer of the CMAC, which reuses relevant data in the prior GMF to more accurately detect tracking errors. This is more general than the local-feedback of a recurrent unit because inputs can simultaneously stir the present state and the prior state to regulate suitable errors. Using a self-organizing algorithm allows increasing or decreasing the layers so that the structure of the new self-organizing fuzzy CMAC (NSOFC) is constructed automatically. The proposed control system consists of a NSOFC and a compensation controller. The NSOFC is the main tracking controller, and imitates an ideal controller; and the compensator expels the approximation error. A Lyapunov stability function is used to make the system stable, and an adaptive proportional integral method allows online updating of the parameters for efficient control. An inverted double pendulum system and a magnetic levitation system are used to demonstrate that the proposed method gives good tracking performance.
AB - This article proposes an efficient intelligent control structure for uncertain nonlinear systems. This controller is a new self-organizing fuzzy cerebellar model articulation controller (CMAC), which has a framework that includes a CMAC and which uses sliding mode control. A new mixed Gaussian membership function (GMF) is created using a prior GMF and a present GMF for each layer of the CMAC, which reuses relevant data in the prior GMF to more accurately detect tracking errors. This is more general than the local-feedback of a recurrent unit because inputs can simultaneously stir the present state and the prior state to regulate suitable errors. Using a self-organizing algorithm allows increasing or decreasing the layers so that the structure of the new self-organizing fuzzy CMAC (NSOFC) is constructed automatically. The proposed control system consists of a NSOFC and a compensation controller. The NSOFC is the main tracking controller, and imitates an ideal controller; and the compensator expels the approximation error. A Lyapunov stability function is used to make the system stable, and an adaptive proportional integral method allows online updating of the parameters for efficient control. An inverted double pendulum system and a magnetic levitation system are used to demonstrate that the proposed method gives good tracking performance.
KW - Cerebellar model articulation controller (CMAC)
KW - fuzzy inference system
KW - inverted double pendulum system
KW - magnetic levitation system
KW - self-organizing technique
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U2 - 10.1109/TIE.2019.2952790
DO - 10.1109/TIE.2019.2952790
M3 - Article
AN - SCOPUS:85088151912
SN - 0278-0046
VL - 67
SP - 9671
EP - 9682
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
IS - 11
M1 - 8910581
ER -