Real-Time Ocean Current Compensation for AUV Trajectory Tracking Control Using a Meta-Learning and Self-Adaptation Hybrid Approach

Yiqiang Zhang; Jiaxing Che; Yijun Hu; Jiankuo Cui; Junhong Cui

doi:10.3390/s23146417

Real-Time Ocean Current Compensation for AUV Trajectory Tracking Control Using a Meta-Learning and Self-Adaptation Hybrid Approach

Sensors (Basel). 2023 Jul 14;23(14):6417. doi: 10.3390/s23146417.

Authors

Yiqiang Zhang¹, Jiaxing Che², Yijun Hu³, Jiankuo Cui¹, Junhong Cui^{1

2}

Affiliations

¹ College of Computer Science and Technology, Jilin University, Changchun 130012, China.
² Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), Shenzhen 518110, China.
³ School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China.

Abstract

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.

Keywords: AUV; adaptive control; meta-learning; trajectory tracking; underwater current.

Grants and funding

This research was supported in part by the National Natural Science Foundation of China under Grant 61971206, Grant 62101211, and Grant U1813217; in part by the National Key R&D Program of China under Grant 2018YFC1405800 and Grant 2021YFC2803000; in part by Overseas Top Talents Program of Shenzhen under Grant KQTD20180411184955957, and Grant LHTD20190004.