| 含能材料反应釜温度模型仿真辨识 |
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| 引用本文： | 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. |
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| 作者姓名： | 陈冲 王冬磊 江沛 尹爱军 张智禹 |
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| 作者单位： | 1.重庆大学 机械工程学院，重庆 400044,2.中国工程物理研究院 化工材料研究所，成都 621900,1.重庆大学 机械工程学院，重庆 400044,1.重庆大学 机械工程学院，重庆 400044,1.重庆大学 机械工程学院，重庆 400044 |
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| 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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| 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。
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| 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 |
| 收稿时间： | 2018/6/4 0:00:00 |
| 修稿时间： | 2018/11/25 0:00:00 |
Simulation and Identification of Reactor Temperature Model for Energetic Materials |
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| CHEN Chong,WANG Dong-lei,JIANG Pei,YIN Ai-jun and ZHANG Zhi-yu.Simulation and Identification of Reactor Temperature Model for Energetic Materials[J].Equipment Environmental Engineering,2018,15(11):85-89. |
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| Authors: | CHEN Chong WANG Dong-lei JIANG Pei YIN Ai-jun and ZHANG Zhi-yu |
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| Institution: | 1. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China,2. Institute of Chemical Materials, China Academy of Engineering Physics, Chengdu 621900, China,1. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China,1. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China and 1. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China |
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| Abstract: | 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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| Keywords: | temperature model reactor segmentation time-varying mutation forgetting factor Recursive Least Squares (RLS) |
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