针对自主式水下航行器（AUV）在海洋中易受海流海浪扰动的问题，本文提出一种基于自抗扰的无模型自适应深度控制方法（ADR-MFAC）。首先，通过引入无模型自适应控制律，解决AUV难以建立精确的数学模型的问题；其次，设计出AUV深度控制器，结合自抗扰控制算法来进一步提高控制器干扰性能，采用跟踪微分器对输入信号进行跟踪，并利用线性状态观测器来估计系统所受扰动。最后，通过仿真和海试验证了ADR-MFAC的控制性能：与PID控制相比，ADR-MFAC在保持较高控制精度的同时，超调量减小了51%，深度调节时间减小了21.4%，证明了ADR-MFAC在受到海流干扰时也可以实现稳定的深度控制，算法具有较强的鲁棒性。
In addressing the challenge of disturbances from ocean currents and waves affecting autonomous underwater vehicles (AUVs), this paper introduces a model-free, adaptive depth control method based on active disturbance rejection control (ADR-MFAC). Firstly, by incorporating model-free adaptive control laws, it resolves the difficulty of establishing precise mathematical models for AUVs. Secondly, an AUV depth controller is designed, combining the active disturbance rejection control algorithm to further enhance the controller's disturbance rejection capabilities. This involves utilizing a tracking differentiator for input signal tracking and leveraging a linear state observer for estimating system disturbances. Finally, the control performance of ADR-MFAC is validated through simulations and sea trials. In comparison to PID control, ADR-MFAC maintains higher control precision while reducing overshoot by 51% and decreasing depth adjustment time by 21.4%. This demonstrates that ADR-MFAC can achieve stable depth control even in the presence of disturbances from ocean currents, highlighting the algorithm's robustness.
2024,46(15): 89-94 收稿日期：2023-10-16
DOI：10.3404/j.issn.1672-7649.2024.15.016
分类号：TP273
基金项目：浙江自然科学基金资助项目（LTGG23E090002）
作者简介：王通（1999 – ），男，硕士研究生，研究方向为水下机器人的运动控制
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