Dynamic Optimal Obstacle Avoidance Control of AUV Formation Based on MLoTFWA Algorithm

Abstract

In addressing the optimal formation obstacle avoidance control problem for Autonomous Underwater Vehicles (AUVs) in environments with unknown and moving obstacles, this paper employs the Modified Fireworks Algorithm based on a Loser Elimination Mechanism (MLoTFWA) and constructs a Distributed Model Predictive Control (DMPC) framework to achieve obstacle avoidance for AUV formations. Initially, a prediction model is established, followed by feedback compensation to mitigate the effects of unknown perturbations. An appropriate fitness function is then formulated, and enhancements such as the loser elimination rule are introduced to optimize the fireworks algorithm. Additionally, the concept of an adaptive DMPC prediction window is proposed to conserve resources. The local and global stability of the DMPC formation control framework is theoretically proven. Simulations verify that the control system based on the DMPC framework ensures safe obstacle avoidance for the formation, maintains formation consistency, and achieves the shortest and smoothest path. The improved fireworks algorithm demonstrates superior performance compared with the original fireworks algorithm and other optimization algorithms. In testing, the improved fireworks algorithm exhibits better adaptability, higher average fitness, and best fitness, along with a significantly faster convergence speed. Compared with the ordinary fireworks algorithm, the convergence speed is reduced by 30%.