On Stable Learning Control Study For Underwater Robot Operation Under Ocean Currents

With the continuous advancement and widespread application of robotics technology, reliable and adaptable control systems have become particularly critical. This strategy can not only improve the stability and operational efficiency of robots in complex interference environments, but also bring significant social benefits, especially in the exploitation of under-water resources. However, in the face of ocean currents, robot systems need to strike a balance between stability, real-time performance, and adaptability. Therefore, this article proposes a stable control strategy with generalization ability in ocean current environments, namely, a learning based robot stability control strategy. Specifically, a control law is designed based on the state offset between the expected state and the actual state to correct the position deviation of the robot. Meanwhile, Lyapunov stability theory is used to ensure the stability of the system. It should be emphasized that this strategy can be extended to different trajectory tracking without the need for repeated parameter adjustments. In addition, to avoid the interference of ocean currents affecting the positioning of the robot itself, we use visual monitoring to obtain the real-time status of the robot. Finally, multiple sets of real experiments are constructed to validate the effectiveness of the proposed strategy.