农业机械学报2026，Vol.57Issue(12)：90-98,110,10.DOI:10.6041/j.issn.1000-1298.2026.12.008
基于改进双Q学习线性自抗扰的水下机器人运动控制研究
Motion Control of Underwater Robot Based on Improved Double Q-Learning and Linear Active Disturbance Rejection Control
摘要
Abstract
Small remotely operated vehicles(ROVs)often suffer from difficulties in accurate model description and weak disturbance rejection in motion control.Although the linear active disturbance rejection controller(LADRC)has been widely used because of its low model dependence and strong disturbance estimation and compensation capability,its control performance may degrade in complex underwater environments when controller parameters remain fixed.To address this problem,an adaptive LADRC parameter tuning method based on an improved double Q-learning algorithm was proposed.A novel piecewise reward function was designed to guide the parameter adjustment process,thereby accelerating convergence and improving control accuracy.Meanwhile,the double Q-learning strategy was introduced to alleviate the overestimation problem in conventional Q-learning training.The proposed method enabled online adjustment of LADRC parameters according to the system state,which enhanced the disturbance observation and compensation capability of the controller.Matlab/Simulink simulations for heading and depth control showed that,compared with conventional LADRC and PID controllers,the proposed double Q-LADRC reduced the maximum overshoot under step disturbances by 1.54%and 39.78%,respectively,and shortened the settling time by 38.2%and 48.4%,respectively.In addition,it achieved faste convergence,low overshoot,and small tracking errors in stabilization control.Pool experiments further verified the effectiveness of the proposed method,demonstrating that it can track the desired signals well and maintain satisfactory control performance.关键词
水下机器人/镇定控制/双Q学习/线性自抗扰控制/参数自适应Key words
underwater robot/stabilization motion/double Q-learning/linear active disturbance rejection control/parameter adaptation分类
信息技术与安全科学引用本文复制引用
周焕银,刘凯伦,刘金生,张子民,葛远香..基于改进双Q学习线性自抗扰的水下机器人运动控制研究[J].农业机械学报,2026,57(12):90-98,110,10.基金项目
江西省科技厅重点基金项目(20224ACB204022)、国家自然科学基金项目(62063001)和江西省研究生省级创新基金项目(YC2024-S505) （20224ACB204022）
