西安石油大学学报（自然科学版）2025，Vol.40Issue(5)：49-56,8.DOI:10.3969/j.issn.1673-064X.2025.05.006
导向钻井稳定平台的DDPG深度强化学习控制
DDPG Deep Reinforcement Learning Control of Stabilized Platform in Rotary Steering Drilling
摘要
Abstract
During the operation of the steering drilling stabilization platform,the tracking effect is not ideal and the robustness is poor due to interference in the system.A deep reinforcement learning control method based on DDPG is proposed to address these issues.The controlled object model and friction model of the stabilization platform were established using the rotary steering drilling stabilization platform as the research object.A stable platform DDPG deep reinforcement learning controller was designed from three aspects:state vector,reward function,and network structure.An Actor-Critic dual network structure was established and the network parameters were-updated.By establishing a nonlinear relationship between the controller input and actual output,the control accuracy,response speed,and anti-interference ability of the stabilization platform were improved.The simulation results of the proposed control method and PID/PIDDOB control methods show that the tracking error of the proposed method is within±10%,and it can effectively suppress parameter perturbations and friction interference.The method has strong robustness and can meet the needs of drilling engineering.关键词
旋转导向钻井/稳定平台/深度强化学习/深度确定性策略梯度Key words
rotary steering drilling/stabilization platform/deep reinforcement learning/deep deterministic policy gradient分类
信息技术与安全科学引用本文复制引用
霍爱清,姜雪,张书涵..导向钻井稳定平台的DDPG深度强化学习控制[J].西安石油大学学报（自然科学版）,2025,40(5):49-56,8.基金项目
陕西省科技厅项目"基于深度强化学习的导向钻井工具智能控制策略研究"(2020GY-152) （2020GY-152）
陕西省教育厅项目"钻井工程的VR沉浸场景动态仿真系统研究"(17JS108) （17JS108）
