| 陈冲,王冬磊,江沛,尹爱军,张智禹.含能材料反应釜温度模型仿真辨识[J].装备环境工程,2018,15(11):85-89. CHEN Chong,WANG Dong-lei,JIANG Pei,YIN Ai-jun,ZHANG Zhi-yu.Simulation and Identification of Reactor Temperature Model for Energetic Materials[J].Equipment Environmental Engineering,2018,15(11):85-89. |
| 含能材料反应釜温度模型仿真辨识 |
| Simulation and Identification of Reactor Temperature Model for Energetic Materials |
| 投稿时间：2018-06-04 修订日期：2018-11-25 |
| DOI：10.7643/ issn.1672-9242.2018.11.016 |
| 中文关键词: 温度模型 反应釜 分段 时变 突变 遗忘因子 递推最小二乘法 |
| 英文关键词:temperature model reactor segmentation time-varying mutation forgetting factor Recursive Least Squares (RLS) |
| 基金项目:国防预研基金项目（9140A17050115JW20001）；重庆市人工智能技术创新重大主题专项重点项目（cstc2017rgzn-zdyfx0007） |
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| Author | Institution |
| CHEN Chong | 1. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China |
| WANG Dong-lei | 2. Institute of Chemical Materials, China Academy of Engineering Physics, Chengdu 621900, China |
| JIANG Pei | 1. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China |
| YIN Ai-jun | 1. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China |
| ZHANG Zhi-yu | 1. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China |
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| 中文摘要: |
| 目的 验证一种基于遗忘因子最小二乘法（FFRLS）的含能材料反应釜温度预测模型辨识方法的正确性和有效性。方法 首先利用基于机理建模方法对系统模型进行分段处理，并得到其具体结构，然后结合历史数据，利用FFRLS对系统模型参数进行辨识，最后得到含能材料反应釜温度仿真模型。结果 在Matlab仿真平台上对该方法的正确性和有效性进行验证，模型参数慢时变状态下该方法辨识参数的模型参数均方根误差（RMSE）皆小于10%，模型参数突变状态下，参数RMSE最小为5.89%，最大为18.69%。结论 该方法能准确、有效地对含能材料反应釜温度模型进行辨识。 |
| 英文摘要: |
| Objective To verify the correctness and effectiveness of method for identifying temperature prediction model of the reactor with energetic materials based on Forgetting Factor Recursive Least-Squares (FFRLS) algorithm. Methods Firstly, the system model was segmented based on the mechanism modeling method, and its specific structure was obtained. Then, combined with historical data, FFRLS was used to identify the parameters of the system model, and finally the reactor temperature simulation model of energetic material was obtained. Results The correctness and effectiveness of the method were verified on the Matlab simulation platform. In this method, the Root Mean Square Error (RMSE) of the time-varying model parameters was less than 10%. When the model parameters were mutated, the minimum RMSE of the parameters was 5.89% and the maximum RMSE was 18.69%. Conclusion This method can accurately and effectively identify the reactor temperature model of energetic materials. |
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