节点文献
基于强化学习的舞台多轴同步系统预测维护策略研究
Research on Predictive Maintenance Strategy for Stage Multi-axis Synchronous System Based on Reinforcement Learning
【摘要】 针对舞台多轴同步系统因执行器退化导致无法满足控制任务时限要求,现有维护策略难以达优的问题,提出一种基于强化学习的舞台多轴同步系统预测维护策略.首先将强化学习以串级方式引入,构建具有寿命预测与自主维护能力,能以不同采样率分而治之的控制架构;其次,聚焦介入维护策略及多源不确定性对执行器退化过程的影响,基于卡尔曼(Kalman)滤波、期望最大化和固定间隔平滑等算法,通过对执行器退化状态的实时感知、估计及退化模型的自适应更新,确保多轴同步系统剩余寿命预测精度;结合系统期望工作时限与剩余寿命预测的偏差、执行器实时退化状态等构建Q-learning算法的目标函数,通过不断试错对维护控制量做出最优调整,以获得最大的寿命延长奖励,从而实现了舞台多轴同步系统智能优化维护.通过舞台多轴同步系统仿真实验验证了所提方法的有效性,提高了系统维护效能.
【Abstract】 Aiming at the problem that the performance of the stage multi-axis synchronous system cannot meet the time limit of the control task due to the degradation of the actuators, and the existing maintenance strategy is difficult to reach the optimization, this paper proposes a reinforcement learning-based predictive maintenance strategy for the stage multi-axis synchronous system. Firstly, reinforcement learning is introduced in a cascaded manner, and constructing a control architecture with capabilities for lifespan prediction and autonomous maintenance, which operates with different sampling rates. Secondly, focusing on the intervening maintenance strategy and the influence of multi-source uncertainty on the actuator degradation process, based on the algorithms of Kalman filtering, Expectation-Maximum, and Rauch-Tung-Striebel smoothing, by the real-time perception and estimation of actuator degradation state, and a daptive update of degradation model, the prediction accuracy of the remaining life of the multi-axis synchronous system is ensured. Combined with the real-time perception, deviation of remaining life prediction, and the actuator degradation state, the objective function of a Q-learning algorithm is constructed. The optimal adjustment of maintenance control is carried out through trials and errors to obtain the maximum life extension reward and realize intelligent optimization maintenance of the stage multi-axis synchronous system. Finally, the effectiveness of the proposed method is verified by simulation experiments of the stage multiaxis synchronous control system, improving the system maintenance efficiency.
【Key words】 stage multi-axis synchronous system; actuator degradation; residual life prediction; reinforcement learning; predictive maintenance;
- 【文献出处】 湖南大学学报(自然科学版) ,Journal of Hunan University(Natural Sciences) , 编辑部邮箱 ,2024年12期
- 【分类号】TP181;TS955
- 【下载频次】10