Adaptive neural network control for double-pendulum tower crane systems

Menghua Zhang; Xingjian Jing

doi:10.1007/978-981-15-7670-6_8

Adaptive neural network control for double-pendulum tower crane systems

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

In practical applications, tower crane systems always exhibit double-pendulum effects, because of non-ignorable hook mass and large payload scale, which makes the model more complicated and most existing control methods inapplicable. Additionally, most available control methods for tower cranes need to linearize the original dynamics and require exact knowledge of system parameters, which may degrade the control performance significantly and make them sensitive to parametric uncertainties. To tackle these problems, a novel adaptive neural network controller is designed based on the original dynamics of double-pendulum tower crane systems without any linear processing. For this reason, the neural network structures are utilized to estimate the parametric uncertainties and external disturbances. Based on the estimated information, and adaptive controller is then designed. The stability of the overall closed-loop system is proved by Lyapunov techniques. Simulation results are illustrated to verify the superiority and effectiveness of the proposed control law.

Original language	English
Title of host publication	Neural Computing for Advanced Applications
Subtitle of host publication	First International Conference, NCAA 2020, Shenzhen, China, July 3–5, 2020, Proceedings
Editors	Haijun Zhang, Zhao Zhang, Zhou Wu, Tianyong Hao
Place of Publication	Singapore
Publisher	Springer
Pages	83-96
ISBN (Electronic)	978-981-15-7670-6
ISBN (Print)	9789811576690
DOIs	https://doi.org/10.1007/978-981-15-7670-6_8
Publication status	Published - 2020
Externally published	Yes
Event	1st International Conference on Neural Computing for Advanced Applications (NCAA 2020) - Shenzhen, China Duration: 3 Jul 2020 → 5 Jul 2020

Publication series

Name	Communications in Computer and Information Science
Volume	1265
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	1st International Conference on Neural Computing for Advanced Applications (NCAA 2020)
Place	China
City	Shenzhen
Period	3/07/20 → 5/07/20

Funding

This work is partially supported by the Innovation and Technology Fund (ITF) Project of HK ITC (Ref. ITP/020/19AP), the Strategic Research Fund of the Research Institute of Urban Sustainable Development, HK Polytechnic University (PolyU), and the Project of Strategic Importance of HK PolyU, the General Research Fund of HK RGC under Grant No. 15206717, the Key Research and Development (Special Public-Funded Projects) of Shandong Province under Grant No. 2019GGX104058, the National Natural Science Foundation for Young Scientists of China under Grant No. 61903155. Acknowledgements. This work is partially supported by the Innovation and Technology Fund (ITF) Project of HK ITC (Ref. ITP/020/19AP), the Strategic Research Fund of the Research Institute of Urban Sustainable Development, HK Polytechnic University (PolyU), and the Project of Strategic Importance of HK PolyU, the General Research Fund of HK RGC under Grant No. 15206717, the Key Research and Development (Special Public-Funded Projects) of Shan-dong Province under Grant No. 2019GGX104058, the National Natural Science Foundation for Young Scientists of China under Grant No. 61903155.

Research Keywords

Adaptive control
Double-pendulum effects
Lyapunov techniques
Neural network
Tower cranes

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10.1007/978-981-15-7670-6_8

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Zhang, M., & Jing, X. (2020). Adaptive neural network control for double-pendulum tower crane systems. In H. Zhang, Z. Zhang, Z. Wu, & T. Hao (Eds.), Neural Computing for Advanced Applications: First International Conference, NCAA 2020, Shenzhen, China, July 3–5, 2020, Proceedings (pp. 83-96). (Communications in Computer and Information Science; Vol. 1265). Springer . https://doi.org/10.1007/978-981-15-7670-6_8

Zhang, Menghua ; Jing, Xingjian. / Adaptive neural network control for double-pendulum tower crane systems. Neural Computing for Advanced Applications: First International Conference, NCAA 2020, Shenzhen, China, July 3–5, 2020, Proceedings. editor / Haijun Zhang ; Zhao Zhang ; Zhou Wu ; Tianyong Hao. Singapore : Springer , 2020. pp. 83-96 (Communications in Computer and Information Science).

@inproceedings{8446e81a4c564626a11c6da49f2a538b,

title = "Adaptive neural network control for double-pendulum tower crane systems",

abstract = "In practical applications, tower crane systems always exhibit double-pendulum effects, because of non-ignorable hook mass and large payload scale, which makes the model more complicated and most existing control methods inapplicable. Additionally, most available control methods for tower cranes need to linearize the original dynamics and require exact knowledge of system parameters, which may degrade the control performance significantly and make them sensitive to parametric uncertainties. To tackle these problems, a novel adaptive neural network controller is designed based on the original dynamics of double-pendulum tower crane systems without any linear processing. For this reason, the neural network structures are utilized to estimate the parametric uncertainties and external disturbances. Based on the estimated information, and adaptive controller is then designed. The stability of the overall closed-loop system is proved by Lyapunov techniques. Simulation results are illustrated to verify the superiority and effectiveness of the proposed control law.",

keywords = "Adaptive control, Double-pendulum effects, Lyapunov techniques, Neural network, Tower cranes",

author = "Menghua Zhang and Xingjian Jing",

year = "2020",

doi = "10.1007/978-981-15-7670-6_8",

language = "English",

isbn = "9789811576690",

series = "Communications in Computer and Information Science",

publisher = "Springer ",

pages = "83--96",

editor = "Haijun Zhang and Zhao Zhang and Zhou Wu and Tianyong Hao",

booktitle = "Neural Computing for Advanced Applications",

note = "1st International Conference on Neural Computing for Advanced Applications (NCAA 2020) ; Conference date: 03-07-2020 Through 05-07-2020",

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Zhang, M & Jing, X 2020, Adaptive neural network control for double-pendulum tower crane systems. in H Zhang, Z Zhang, Z Wu & T Hao (eds), Neural Computing for Advanced Applications: First International Conference, NCAA 2020, Shenzhen, China, July 3–5, 2020, Proceedings. Communications in Computer and Information Science, vol. 1265, Springer , Singapore, pp. 83-96, 1st International Conference on Neural Computing for Advanced Applications (NCAA 2020), Shenzhen, China, 3/07/20. https://doi.org/10.1007/978-981-15-7670-6_8

Adaptive neural network control for double-pendulum tower crane systems. / Zhang, Menghua; Jing, Xingjian.
Neural Computing for Advanced Applications: First International Conference, NCAA 2020, Shenzhen, China, July 3–5, 2020, Proceedings. ed. / Haijun Zhang; Zhao Zhang; Zhou Wu; Tianyong Hao. Singapore: Springer , 2020. p. 83-96 (Communications in Computer and Information Science; Vol. 1265).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Adaptive neural network control for double-pendulum tower crane systems

AU - Zhang, Menghua

AU - Jing, Xingjian

PY - 2020

Y1 - 2020

N2 - In practical applications, tower crane systems always exhibit double-pendulum effects, because of non-ignorable hook mass and large payload scale, which makes the model more complicated and most existing control methods inapplicable. Additionally, most available control methods for tower cranes need to linearize the original dynamics and require exact knowledge of system parameters, which may degrade the control performance significantly and make them sensitive to parametric uncertainties. To tackle these problems, a novel adaptive neural network controller is designed based on the original dynamics of double-pendulum tower crane systems without any linear processing. For this reason, the neural network structures are utilized to estimate the parametric uncertainties and external disturbances. Based on the estimated information, and adaptive controller is then designed. The stability of the overall closed-loop system is proved by Lyapunov techniques. Simulation results are illustrated to verify the superiority and effectiveness of the proposed control law.

AB - In practical applications, tower crane systems always exhibit double-pendulum effects, because of non-ignorable hook mass and large payload scale, which makes the model more complicated and most existing control methods inapplicable. Additionally, most available control methods for tower cranes need to linearize the original dynamics and require exact knowledge of system parameters, which may degrade the control performance significantly and make them sensitive to parametric uncertainties. To tackle these problems, a novel adaptive neural network controller is designed based on the original dynamics of double-pendulum tower crane systems without any linear processing. For this reason, the neural network structures are utilized to estimate the parametric uncertainties and external disturbances. Based on the estimated information, and adaptive controller is then designed. The stability of the overall closed-loop system is proved by Lyapunov techniques. Simulation results are illustrated to verify the superiority and effectiveness of the proposed control law.

KW - Adaptive control

KW - Double-pendulum effects

KW - Lyapunov techniques

KW - Neural network

KW - Tower cranes

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U2 - 10.1007/978-981-15-7670-6_8

DO - 10.1007/978-981-15-7670-6_8

M3 - RGC 32 - Refereed conference paper (with host publication)

AN - SCOPUS:85089724028

SN - 9789811576690

T3 - Communications in Computer and Information Science

SP - 83

EP - 96

BT - Neural Computing for Advanced Applications

A2 - Zhang, Haijun

A2 - Zhang, Zhao

A2 - Wu, Zhou

A2 - Hao, Tianyong

PB - Springer

CY - Singapore

T2 - 1st International Conference on Neural Computing for Advanced Applications (NCAA 2020)

Y2 - 3 July 2020 through 5 July 2020

ER -

Zhang M, Jing X. Adaptive neural network control for double-pendulum tower crane systems. In Zhang H, Zhang Z, Wu Z, Hao T, editors, Neural Computing for Advanced Applications: First International Conference, NCAA 2020, Shenzhen, China, July 3–5, 2020, Proceedings. Singapore: Springer . 2020. p. 83-96. (Communications in Computer and Information Science). Epub 2020 Aug 13. doi: 10.1007/978-981-15-7670-6_8

Adaptive neural network control for double-pendulum tower crane systems

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