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Research Papers

A Task-Space Tracking Control Approach for Duct Cleaning Robot Based on Fuzzy Wavelet Neural Network

[+] Author and Article Information
Bu Dexu

College of Electrical and
Information Engineering,
Hunan University,
Changsha 410082, China
e-mail: yuying4000@163.com

Kong Weiwei

School of Vehicle and Mobility,
Tsinghua University,
Beijing 100082, China
e-mail: kongweiwei@mail.tsinghua.edu.cn

Qi Yunlong

School of Vehicle and Mobility,
Tsinghua University,
Beijing 100082, China
e-mail: yunlongqi@163.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received November 6, 2018; final manuscript received May 26, 2019; published online July 1, 2019. Assoc. Editor: Mohammad A. Ayoubi.

J. Dyn. Sys., Meas., Control 141(11), 111004 (Jul 01, 2019) (14 pages) Paper No: DS-18-1495; doi: 10.1115/1.4043933 History: Received November 06, 2018; Revised May 26, 2019

In this study, a task-space adaptive robust control methodology which takes uncertainties and external disturbances into account is proposed for a class of duct cleaning mobile manipulators. First of all, the configuration of the real duct cleaning robot is introduced, and the Jacobian matrix and the dynamic model of the real robotic system are obtained. Then, the structure of adaptive robust controller based on sliding mode control (SMC) approach and the fuzzy wavelet neural network is detailed, the proposed control approach combines the advantages of SMC which can suppress the external disturbances with the fuzzy wavelet neural network which can compensate the uncertainties by its strong ability to approximate a nonlinear function to an arbitrary accuracy, the stability of the whole robotic control system, the uniformly ultimately boundedness of tracking errors, and the boundedness of fuzzy wavelet neural networks weight estimation errors are all guaranteed based on the Lyapunov stability theory. Finally, simulation results are presented to demonstrate the superior performance of the proposed approach, and experiments are given to illustrate that the proposed approach is useful for real duct cleaning robot system with well performance.

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Figures

Grahic Jump Location
Fig. 1

(a) The traditional approach of cleaning duct, ((b) and (c)) are common duct cleaning robots, and (d) is the proposed duct cleaning robot

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Fig. 2

(a) The diagram of the working environment of duct cleaning robot and (b) the main obstacles of the duct

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Fig. 3

The structure model of duct cleaning robot

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Fig. 4

Fuzzy wavelet neural network structure

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Fig. 5

The structure of proposed control strategy

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Fig. 6

The tracking performances of the SMC scheme and proposed strategy: (a) tracking performance of x, (b) tracking performance of y, (c) tracking performance of xE, (d) tracking performance of yE, and (e) tracking performance of zE

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Fig. 7

The tracking errors of the SMC scheme and proposed strategy: (a) tracking error of x, (b) tracking error of y, (c) tracking error of xE, (d) tracking error of yE, and (e) tracking error of zE

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Fig. 8

The control torques of the SMC and proposed strategy: (a) control torques of x and y and (b) control torques of xE, yE, and zE

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Fig. 9

Experimental equipment and environment: (a) electrical control system and real equipments of duct cleaning robot and (b) experimental environment of duct cleaning robot

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Fig. 10

Experimental results of proposed control scheme: (a) end-effector tracking performance of experimental results, (b) tracking performance of xE, (c) tracking performance of yE, (d) tracking performance of zE, (e) tracking performance of x, and (f) tracking performance of y

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Fig. 11

Tracking errors of experiment by proposed control scheme: (a) tracking errors of xE, yE, and zE and (b) tracking errors of x and y

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Fig. 12

Control torques of experiment by the proposed control scheme: (a) control torques of xE, yE, and zE and (b) control torques of x and y

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