Underactuated AUV backstepping sliding mode horizontal trajectory tracking control based on RBF neural network

WANG Chu; WANG Lei; HU Zhen; HU Baoqiang

doi:10.11993/j.issn.2096-3920.2025-0076

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WANG Chu, WANG Lei, HU Zhen, HU Baoqiang. Underactuated AUV backstepping sliding mode horizontal trajectory tracking control based on RBF neural network[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0076

Citation:

WANG Chu, WANG Lei, HU Zhen, HU Baoqiang. Underactuated AUV backstepping sliding mode horizontal trajectory tracking control based on RBF neural network[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0076

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Underactuated AUV backstepping sliding mode horizontal trajectory tracking control based on RBF neural network

doi: 10.11993/j.issn.2096-3920.2025-0076

China Ship Scientific Research Center, Wuxi 214082, China

Received Date: 2025-06-04
Accepted Date: 2025-07-22
Rev Recd Date: 2025-07-05

Available Online: 2025-11-24

Abstract

Abstract

A backstepping integral sliding mode trajectory tracking control method for underactuated autonomous underwater vehicles (AUV) based on radial basis function (RBF) neural network was proposed to address the challenges of difficult horizontal trajectory tracking control and weak anti-interference ability in complex marine environments. Firstly, a kinematic controller was designed by employing the backstepping control method to obtain virtual control laws and actual control inputs. In the dynamic controller, integral sliding mode control was introduced to account for the uncertainty factors and possible external disturbances of the system. Meanwhile, an RBF neural network was adopted to approximate the unknown nonlinear terms of the system online, effectively resolving the contradiction between the chattering effect and parameter uncertainty in traditional sliding mode control. By taking the error as the input of the RBF neural network and using the output of the RBF neural network as the switching control, the online adjustment of the sliding mode control law was achieved. The simulation results show that, compared with the traditional backstepping sliding mode control, the proposed method can effectively eliminate the "chattering" problem caused by the switching terms in traditional sliding mode control, enabling the system to exhibit fast dynamic response and strong robustness.
- autonomous undersea vehicle,
- trajectory tracking,
- RBF neural network,
- backstepping sliding mode control

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References(14)

References

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