Position Tracking Control of 4-DOF Underwater Robot Leg Using Deep Learning

Abstract

This paper presents a novel hybrid control method for position tracking of an underwater quadruped walking robot. The proposed approach combines an existing position-tracking control method with a deep-learning neural network. The neural network compensates for non-linear dynamic characteristics, such as the effect of fluid, without relying on mathematical modeling. To achieve this, a Multi-Layer Perceptron neural network is designed to analyze joint torque in relation to the joint angle and angular velocity of the robot, as well as the position and orientation of the foot tip and environmental data. The improvement in tracking control performance is evaluated using a 4-DOF underwater robot leg. For the neural network design, position tracking control data, including dynamic characteristics, were collected through position command-based position tracking control. Afterward, a learning model was constructed and trained to predict joint torque related to the robot’s motion and posture. This learning process incorporates non-linear dynamic characteristics, such as joint friction and the influence of fluid, in the joint torque prediction. The proposed method is then combined with conventional task-space PD control to perform position-tracking control with enhanced performance. Finally, the proposed method is evaluated using the underwater robot leg and compared to a single task-space PD controller. The proposed method demonstrates higher position accuracy with similar joint torque output, thereby increasing compliance and tracking performance simultaneously.