哈尔滨工程大学学报2025，Vol.46Issue(7)：1340-1348,9.DOI:10.11990/jheu.202405011
基于强化学习自抗扰的气垫船进坞控制策略
Docking control strategy of air cushion vehicle with active disturbance rejection based on reinforcement learning
摘要
Abstract
To address the issues of large errors,slow response,and high-risk collision during the docking process of air cushion vehicles,a nonlinear active disturbance rejection controller was designed using the deterministic poli-cy gradient algorithm in reinforcement learning.To realize a control strategy for the docking process that controls the heading,speed,and lateral displacement of vehicles,optimized active disturbance rejection and proportional inte-gral derivative controls were combined.The simulation results demonstrate that this control strategy not only a-chieves rapid tracking of the target heading but also enhances the robustness of heading control against uncertain disturbances,thereby ensuring both the speed and precision of the docking process.关键词
全垫升气垫船/非线性自抗扰控制/强化学习/确定性策略梯度/神经网络/PID控制/艏向控制/航速控制/外界扰动Key words
air cushion vehicle/nonlinear active disturbance rejection control/reinforcement learning/determinis-tic strategy gradient/neural network/PID control/heading control/speed control/external disturbance分类
交通工程引用本文复制引用
王元慧,张峻恺,吴鹏..基于强化学习自抗扰的气垫船进坞控制策略[J].哈尔滨工程大学学报,2025,46(7):1340-1348,9.基金项目
国家自然科学基金项目(52471377). （52471377）
