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Research on UUV Recovery Active Disturbance Rejection Control Based on LMNN Compensation

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Abstract

As a kind of intelligent marine equipment, its successful recovery is the basis of Unmanned Underwater Vehicle (UUV)’s normal working and completing the mission successfully. However, the mathematical model of UUV is severely coupled and highly nonlinear, and may be subject to complex interference in the recovery process. Besides, the model could not be determined completely. Active disturbance rejection control (ADRC) method does not need the beforehand information of the unknown disturbance and also can ensure the stability. But conventional ADRC method with fixed parameters could not adjust to UUV complicated motion control. This paper introduces the adaptive wavelet neural network optimized by Levenberg-Marquardt algorithm, and puts forward a novel ADRC control method improved by wavelet neural network algorithm and Levenberg-Marquardt algorithm, for partial system identification and uncertain model compensation. Moreover, with pitching moment change considered in the process of UUV recovery, two dimensionless hydrodynamic coefficients are defined based on near-wall effect. The simulation experiments have been tested to verify the effectiveness of the proposed control. The results indicate that the ADRC with Levenberg-Marquardt neural network could control UUV recovery process in the variable disturbance environment more stable and reduce the ADRC computational burden.

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Authors and Affiliations

  1. College of Automation, Harbin Engineering University, No.145 Nantong Street, Nangang District, Harbin, Heilongjiang Province, China

    Xue Du, Wenhua Wu, Wei Zhang & Shouyi Hu

Authors
  1. Xue Du
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  2. Wenhua Wu
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  3. Wei Zhang
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  4. Shouyi Hu
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Corresponding author

Correspondence to Wei Zhang.

Additional information

This was supported by Natural Science Foundation of China (51709062), Equipment Preresearch Key Lab Fund(6142215180107), China Postdoctoral Science Foundation (2019M651265), Harbin Science and Technology Talent Research Special Fund (2017RAQXJ150) and Natural Science Foundation of Heilongjiang Province (LH2020E079).

Xue Du received her B.S. degree in the measurement, control technology and instrument from Harbin Engineering University in 2010, an M.S. degree in control engineering from Harbin Engineering University in 2012, and a Ph.D. degree in navigation, guidance and control from Harbin Engineering University in 2016. She is working as a post-doctoral researcher in naval architecture and marine engineering in Harbin Engineering University from 2018. She is currently an associate professor in the College of Automation in Harbin Engineering University. Her research interests include unmanned underwater vehicle navigation and control, unmanned underwater cooperative navigation and control, and underwater image processing.

Wenhua Wu received his B.S. degree in electrical engineering and its automation from Jiamusi University in 2018. He is currently pursuing an M.S. degree in control science and engineering at Harbin Engineering University. His research interest is intelligent control of underwater unmanned vehicle.

Wei Zhang received his B.S. degree in heat engine professional from Jiangsu University of Science and Technology in 2001. He received an M.S. degree in thermal engineering from Harbin Engineering University in 2004 and a Ph.D. degree in control theory and control engineering from Harbin Engineering University in 2006. From 2008 to 2016, he was an associate professor in the College of Automation in Harbin Engineering University. Since 2016, he has been a Professor with the College of Automation in Harbin Engineering University. He is the author of more than 50 articles. His research interests include unmanned underwater vehicle recovery control, overall design of underwater vehicle and data fusion.

Shouyi Hu received his B.Sc. degree from Shenyang ligong University in 2012. Now, he is an M.Sc. candidate in Harbin Engineering University. His main research interest includes intelligent control of underwater unmanned vehicle.

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Du, X., Wu, W., Zhang, W. et al. Research on UUV Recovery Active Disturbance Rejection Control Based on LMNN Compensation. Int. J. Control Autom. Syst. 19, 2569–2582 (2021). https://doi.org/10.1007/s12555-019-0977-5

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  • DOI: https://doi.org/10.1007/s12555-019-0977-5

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