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Research Papers

Design of Adaptive Fuzzy Control for a Class of Networked Nonlinear Systems

[+] Author and Article Information
Mohamed Hamdy

Industrial Electronics and
Control Engineering Department,
Faculty of Electronic Engineering,
Menoufia University,
Menouf 32952, Egypt
e-mail: mohamed.hamdy@el-eng.menofia.edu.eg

Sameh Abd-Elhaleem

Industrial Electronics and
Control Engineering Department,
Faculty of Electronic Engineering,
Menoufia University,
Menouf 32952, Egypt
e-mail: sameh.abdelhaleem@el-eng.menofia.edu.eg

M. A. Fkirin

Industrial Electronics and
Control Engineering Department,
Faculty of Electronic Engineering,
Menoufia University,
Menouf 32952, Egypt
e-mail: mafkirin@el-eng.menofia.edu.eg

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 18, 2016; final manuscript received October 3, 2016; published online January 16, 2017. Assoc. Editor: Zongxuan Sun.

J. Dyn. Sys., Meas., Control 139(3), 031008 (Jan 16, 2017) (9 pages) Paper No: DS-16-1036; doi: 10.1115/1.4034947 History: Received January 18, 2016; Revised October 03, 2016

This paper presents an adaptive fuzzy controller for a class of unknown nonlinear systems over network. The network-induced delays can degrade the performance of the networked control systems (NCSs) and also can destabilize the system. Moreover, the seriousness of the delay problem is aggravated when packet losses occur during a transmission of data. The proposed controller uses a filtered tracking error to cope the time-varying network-induced delays. It is also robust enough to cope some packet losses in the system. Fuzzy logic systems (FLSs) are used to approximate the unknown nonlinear functions that appear in the tracking controller. Based on Lyapunov stability theory, the constructed controller is proved to be asymptotically stable. Stability of the adaptive fuzzy controller is guaranteed in the presence of bounded external disturbance, time-varying delays, and data packet dropouts. Simulated application of the inverted pendulum tracking illustrates the effectiveness of the proposed technique with comparative results.

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Figures

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Fig. 1

The block diagram for the proposed adaptive fuzzy controller

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Fig. 3

Tracking response of the inverted pendulum: (a) x1(t), (b) x2(t), (c) u(t), (d) time-varying network-induced delays, (e) e(t), and (f) e˙(t)

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Fig. 6

Tracking response of the inverted pendulum under angle disturbance: (a) x1(t) and (b) u(t)

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Fig. 7

Regulation response of the inverted pendulum under angle disturbance: (a) x1(t) and (b) u(t)

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Fig. 8

Tracking response of the inverted pendulum disturbance (a) x1(t) and (b) u(t)

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Fig. 9

Regulation response of the inverted pendulum under input disturbance: (a) x1(t) and (b) u(t)

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Fig. 4

Regulation response of the inverted pendulum: (a) x1(t), (b) x2(t), (c) u(t), and (d) time-varying network-induced delays

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Fig. 5

External disturbance for the inverted pendulum d1: angle disturbance and d2: input disturbance

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Fig. 2

Inverted pendulum on a cart

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