Zhang Yiqiang, Che Jiaxing, Hu Yijun, Cui Jiankuo, Cui Junhong
College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), Shenzhen 518110, China.
Sensors (Basel). 2023 Jul 14;23(14):6417. doi: 10.3390/s23146417.
Autonomous underwater vehicles (AUVs) may deviate from their predetermined trajectory in underwater currents due to the complex effects of hydrodynamics on their maneuverability. Model-based control methods are commonly employed to address this problem, but they suffer from issues related to the time-variability of parameters and the inaccuracy of mathematical models. To improve these, a meta-learning and self-adaptation hybrid approach is proposed in this paper to enable an underwater robot to adapt to ocean currents. Instead of using a traditional complex mathematical model, a deep neural network (DNN) serving as the basis function is trained to learn a high-order hydrodynamic model offline; then, a set of linear coefficients is adjusted dynamically by an adaptive law online. By conjoining these two strategies for real-time thrust compensation, the proposed method leverages the potent representational capacity of DNN along with the rapid response of adaptive control. This combination achieves a significant enhancement in tracking performance compared to alternative controllers, as observed in simulations. These findings substantiate that the AUV can adeptly adapt to new speeds of ocean currents.
自主水下航行器（AUV）在水下水流中可能会因流体动力学对其机动性的复杂影响而偏离预定轨迹。基于模型的控制方法通常用于解决这个问题，但它们存在与参数时变性和数学模型不准确性相关的问题。为了改进这些问题，本文提出了一种元学习和自适应混合方法，以使水下机器人能够适应洋流。该方法不使用传统的复杂数学模型，而是训练一个作为基函数的深度神经网络（DNN）来离线学习高阶流体动力学模型；然后，通过自适应律在线动态调整一组线性系数。通过将这两种实时推力补偿策略相结合，所提出的方法利用了DNN强大的表征能力以及自适应控制的快速响应。如模拟所示，与替代控制器相比，这种组合在跟踪性能上有显著提高。这些结果证实了AUV能够熟练地适应新的洋流速度。
