Neural Network-Augmented Actuation Control System Designed for Path Tracking of Autonomous Underwater-Transportation Systems Under Sensor and Process Noise

Underwater-transportation systems have significant potential for both military and commercial applications. Neural Network (NN)-based control offers enhanced robustness for actuators to manage the states of autonomous underwater-transportation systems which include Rigid-Connection Transportation Systems (RCTSs), Flexible-Connection Transportation Systems (FCTSs) and Leader–Follower-Formation Control Transportation Systems (LFFCTSs). In this study, NN-Augmented Control (NNAC) is applied to the aforementioned three transportation systems to enable accurate path tracking by the actuators installed onboard these systems under both ideal operating conditions and in the presence of sensor and process noise. The Extended Kalman Filter (EKF) is employed to estimate the system states under noisy conditions. The results demonstrate that NNAC provides robust and adaptive control of actuators, achieving efficient trajectory tracking via the transportation systems despite the influence of sensor and process noise disturbances. NNAC predominance was also observed in comparison with the conventional PID controller. Among the transportation configurations under the NNAC strategy, the RCTS exhibited the highest tracking accuracy with the lowest power consumption by the actuators. The power consumption of actuators installed on the LFFCTS was marginally higher than that of the RCTS. However, the translational motion accuracy of the follower vehicle in the LFFCTS was the lowest due to indirect actuation control through the formation controller. In contrast, actuators in the FCTS showed the highest power consumption while motion accuracy was comparatively lowest, attributed to the increased complexity of its dynamic positioning requirements.