Work

Convex Optimization-Based Adaptive Neural Network Control for Unmanned Surface Vehicles Considering Moving Obstacles

Year: 2025

Type: article

Abstract: This article addresses the challenge of designing obstacle avoidance control strategies for unmanned ship systems operating in environments with moving obstacles and unmodeled dynamics. First, we utilize an enhanced artificial potential field method to generate real-time paths that allow unmanned ships to avoid obstacles effectively, overcoming the design challenges posed by moving obstacles. Next, we incorporate convex optimization techniques to create a novel adaptive neural network control strategy aimed at tackling potential dynamic uncertainties in unmanned ship systems. Finally, we present simulation results that demonstrate the effectiveness of the proposed dynamic obstacle avoidance control strategy.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse13030587

Authors: Dongxiao Liu, Jiapeng Liu, Chongwei Sun, Baobin Dai

Institution: Qingdao University

Language: English

Topic: Maritime Navigation and Safety

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Natural Science Foundation of China