Adaptive Neural Network-Based Prescribed Performance Control of AUVs with Input Saturation

XU Wenfeng; LIU Jiapeng; YU Jinpeng; HAN Yaning

doi:10.11993/j.issn.2096-3920.2023-0041

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XU Wenfeng, LIU Jiapeng, YU Jinpeng, HAN Yaning. Adaptive Neural Network-Based Prescribed Performance Control of AUVs with Input Saturation[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0041

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XU Wenfeng, LIU Jiapeng, YU Jinpeng, HAN Yaning. Adaptive Neural Network-Based Prescribed Performance Control of AUVs with Input Saturation[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0041

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Adaptive Neural Network-Based Prescribed Performance Control of AUVs with Input Saturation

doi: 10.11993/j.issn.2096-3920.2023-0041

1.
School of Automation, Qingdao University, Shandong Qingdao 266071, China
2.
Shandong Provincial Key Laboratory of Industrial Control Technology, Shandong Qingdao 266071, China

Received Date: 2023-04-18
Accepted Date: 2023-07-12
Rev Recd Date: 2023-05-21

Available Online: 2023-12-13

Abstract

Abstract

Aiming at the problems of system uncertainty and input saturation of autonomous underwater vehicle(AUV), an improved adaptive neural network-based prescribed performance control strategy is proposed to track the desired trajectory. Firstly, nonlinear transformation is introduced to ensure that the position error remains within the preset time-varying range, improving control accuracy. Based on backstepping and Lyapunov functions, a virtual control law for the system is designed. Then, the neural network technology is used to process the unknown parameters of the system model, and the real control law of the system is reconstructed, which simplifies the traditional backstepping control strategy and effectively reduces the computational complexity. Then, based on the Lyapunov stability theory, the error signals of AUV system are all bounded. Finally, compared with traditional dynamic surface control methods, simulation results show that the proposed control strategy has better control performance and can effectively overcome the impact of uncertainty on system performance when considering input saturation, achieving effective tracking of target trajectories.
- autonomous underwater vehicle,
- neural network,
- backstepping control,
- trajectory tracking

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References(19)

References

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