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Research Papers

Adaptive Robust Backstepping Output Tracking Control for a Class of Uncertain Nonlinear Systems Using Neural Network

[+] Author and Article Information
Yuchao Wang

College of Mechanical and
Electrical Engineering,
Sichuan Agricultural University,
Yaan 625014, China
e-mail: wangyc0918@yahoo.co.jp

Lijia Xu

College of Mechanical and
Electrical Engineering,
Sichuan Agricultural University,
Yaan 625014, China
e-mail: lijiaxu13@hotmail.com

Hansheng Wu

Department of Information Science,
Prefectural University of Hiroshima,
Hiroshima 734-8558, Japan
e-mail: hansheng@pu–hiroshima.ac.jp

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received September 29, 2016; final manuscript received November 30, 2017; published online March 7, 2018. Assoc. Editor: Srinivasa M. Salapaka.

J. Dyn. Sys., Meas., Control 140(7), 071014 (Mar 07, 2018) (10 pages) Paper No: DS-16-1468; doi: 10.1115/1.4039151 History: Received September 29, 2016; Revised November 30, 2017

The problem of robust output tracking is studied for a class of uncertain nonlinear systems in the presence of structure uncertainties, external disturbances, and unknown time-varying virtual control coefficients. In this study, it is supposed that the upper bounds of external disturbances and that the upper and lower bounds of unknown time-varying virtual control coefficients are unknown. By employing a simple structure neural network (NN), the unknown structure uncertainties are approximated. A class of backstepping approach-based adaptive robust controllers is synthesized for such uncertain nonlinear systems. By making use of Lyapunov functional approach, it is also shown that the proposed adaptive robust backstepping output tracking controller can guarantee the tracking error between the system output and the desired reference signal to converge asymptotically to zero. Finally, two numerical examples are given to demonstrate the effectiveness of the proposed controller.

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Figures

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Fig. 5

Example 1: Tracking error e1(t) (using the method of Ref. [24])

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Fig. 6

Example 1: History of updating parameters (using the method of Ref. [24])

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Fig. 7

Schematic diagram of a single-link manipulator actuated by a DC motor

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Fig. 1

Example 1: Output y(t) and reference trajectory yd(t) (using our method)

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Fig. 2

Example 1: Tracking error e1(t) (using our method)

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Fig. 3

Example 1: History of updating parameters (using our method)

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Fig. 4

Example 1: Output y(t) and reference trajectory yd(t) (using the method of Ref. [24])

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Fig. 8

Example 2: Position q(t) and reference signal yd(t) (using our method)

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Fig. 9

Example 2: Tracking error e1(t) (using our method)

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Fig. 10

Example 2: History of updating parameters (using our method)

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Fig. 11

Example 2: Position q(t) and reference signal yd(t) (using the method of Ref. [24])

Grahic Jump Location
Fig. 12

Example 2: Tracking error e1(t) (using the method of Ref. [24])

Grahic Jump Location
Fig. 13

Example 2: History of updating parameters (using the method of Ref. [24])

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