To address the challenges in multi-AUV formation maneuvering, such as limited state perception and transmission capabilities, acoustic communication delays, data loss, and reduced observability due to the lack of position information exchange, this study proposes an event-triggered metrology−communication unified framework with a Lyapunov-based model predictive formation control method (ETMCU−LMPC). The proposed approach aims to enhance formation stability and tracking accuracy.

First, by integrating the formation communication topology with system states, an event-triggered mechanism based on state observation is established. This mechanism leverages relative measurements among AUVs to mitigate delays and data loss caused by acoustic communication failures, while improving system observability in the absence of position information exchange. Second, a distributed model predictive controller based on Lyapunov theory is designed. The controller employs backstepping to construct contractive constraints, ensuring recursive feasibility, and incorporates adaptive Kalman filtering (AKF) to compensate for measurement noise, thereby guaranteeing closed-loop stability.

Simulation results of the formation control for five AUVs (1 leader and 4 followers) show that, compared with the traditional LMPC, the proposed ETMCU−LMPC method reduces the convergence time from 8 s to 6 s, the maximum error from 1.12 m to 0.36 m, and the steady-state error from 0.57 m to 0.06 m. Additionally, the control input exhibits greater stability.

The proposed method can effectively cope with communication anomalies, improve the reliability of multi-AUV formations under scenarios with limited state perception and transmission, and thus possesses practical engineering significance.

This paper seeks to provide a solution for the formation control issue that arises when autonomous underwater vehicles (AUVs) are subjected to interference from obstacles and complex ocean currents.

To tackle the issue of AUV hysteresis resulting from an overly rapid predicted convergence speed during dynamic obstacle avoidance, a multi-AUV formation adaptive control method (NDP-ABS) based on brain dynamics model prediction is created. Active and inhibitory sources are created to solve the local optimization problem of potential field methods. When paired with optimal control, dynamic obstacle avoidance, formation control, and predicted tracking are accomplished. Second, a nonlinear adaptive backstepping method is used to design the AUV expected tracking controller, which resolves the interference of shallow ocean current disturbances and nonlinear factors on the AUV expected tracking control in consideration of unknown nonlinear factors and ocean current disturbances introduced in the control law of the NDP process. Finally, Lyapunov theory is used to demonstrate the system's stability.

The anti-interference and obstacle avoidance performance of the NDP-ABS system are tested in six sets of comparative simulation tests, and the results confirm its efficacy.

The NDP-ABS formation scheme offers several benefits, including cheap obstacle avoidance costs, robust resistance to interference from ocean currents, high stability, and clear advantages in the non-explicit formation control of multiple AUVs.