中国舰船研究2025，Vol.20Issue(1)：326-339,14.DOI:10.19693/j.issn.1673-3185.04045
基于神经动力学模型预测的多AUV编队自适应控制方案
A neural dynamics model prediction-based adaptive control system for AUV formation control
摘要
Abstract
[Objectives]This paper seeks to provide a solution for the formation control issue that arises when autonomous underwater vehicles(AUVs)are subjected to interference from obstacles and complex ocean currents.[Methods]To tackle the issue of AUV hysteresis resulting from an overly rapid predicted convergence speed during dynamic obstacle avoidance,a multi-AUV formation adaptive control method(NDP-ABS)based on brain dynamics model prediction is created.Active and inhibitory sources are created to solve the local optimization problem of potential field methods.When paired with optimal control,dynamic obstacle avoidance,formation control,and predicted tracking are accomplished.Second,a nonlinear adaptive backstepping method is used to design the AUV expected tracking controller,which resolves the interference of shallow ocean current disturbances and nonlinear factors on the AUV expected tracking control in consider-ation of unknown nonlinear factors and ocean current disturbances introduced in the control law of the NDP process.Finally,Lyapunov theory is used to demonstrate the system's stability.[Results]The anti-interfer-ence and obstacle avoidance performance of the NDP-ABS system are tested in six sets of comparative simula-tion tests,and the results confirm its efficacy.[Conclusions]The NDP-ABS formation scheme offers sev-eral benefits,including cheap obstacle avoidance costs,robust resistance to interference from ocean currents,high stability,and clear advantages in the non-explicit formation control of multiple AUVs.关键词
自主水下航行器/运动控制/编队控制/生物启发/模型预测/自适应反步Key words
autonomous underwater vehicles/motion control/formation control/biological inspiration/model prediction/adaptive backstepping分类
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张继文,徐博,王储岩,王朝阳..基于神经动力学模型预测的多AUV编队自适应控制方案[J].中国舰船研究,2025,20(1):326-339,14.基金项目
微系统技术重点实验室开放课题(6142804230106) （6142804230106）
海南省重点研发项目(ZDYF2024GXJS009) （ZDYF2024GXJS009）
