| 引用本文: | 董泽,尹二新.基于状态观测与教学优化算法的多变量系统历史数据驱动辨识[J].控制理论与应用,2017,34(10):1369~1379.[点击复制] |
| DONG Ze,YIN Er-xin.Historical data driven identification for multivariable systems based on state observation and teaching-learning-based optimization algorithm[J].Control Theory and Technology,2017,34(10):1369~1379.[点击复制] |
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| 基于状态观测与教学优化算法的多变量系统历史数据驱动辨识 |
| Historical data driven identification for multivariable systems based on state observation and teaching-learning-based optimization algorithm |
| 摘要点击 1835 全文点击 985 投稿时间：2016-12-13 修订日期：2017-08-23 |
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| DOI编号 10.7641/CTA.2017.60942 |
| 2017,34(10):1369-1379 |
| 中文关键词 扰动 状态观测器 教学优化算法 多变量系统 历史数据驱动 辨识 |
| 英文关键词 disturbance state observer teaching-learning-based optimization algorithm multivariable systems historical data driven identification |
| 基金项目 国家自然科学基金项目(71471060), 山西省煤基重点科技攻关项目(MD2014--03--06--02) |
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| 中文摘要 |
| 常规智能算法与历史数据结合进行多变量系统辨识的方法, 选取表征系统由稳态过渡到动态过程的数据
作建模数据, 当该过程含有未知扰动时, 无法准确建立对象模型. 本文提出一种基于状态观测与教学优化算法的多
变量系统历史数据驱动辨识方法. 该方法选取系统由动态回归稳态的历史数据, 并根据其稳态终值进行去稳态分
量处理. 再将其分为两段, 应用状态观测器与预估模型对第1段数据末端的系统状态进行估计, 并将估计值作为第2
段数据对应的系统初态; 应用第2段数据的输入对预估模型进行仿真, 采用教学优化算法寻优预估模型参数, 使仿
真输出接近实际输出. 仿真实验表明该方法可以克服扰动对模型辨识精度的影响. 最后对某火电机组协调控制系统
进行建模, 结果表明了该方法的有效性. |
| 英文摘要 |
| The conventional multivariable system identification method based on the combination of intelligent algorithms
and historical data selects historical data, which represent the system from steady-state to dynamic-state, as modeling
data. When the modeling data contain unknown disturbance, this method cannot establish the correct system model.
Therefore, a historical data driven identification method for multivariable systems based on state observation and teachinglearning-
based optimization algorithm is proposed. In this method, historical data representing the system changing from
dynamic-state to steady-state are treated as modeling data. The steady-state component is removed based on final steadystate
value. Then the data are divided into two segments. The system status at the end of the first segment is obtained by
means of state observer and prediction model, then it serves as the initial system status of the second segment. Input data
of the second segment and the prediction model are employed to simulate the system. And in order to make the simulation
output close to the actual output, teaching-learning-based optimization algorithm is adopted to optimize the prediction
model parameters. In the modeling simulation of a multivariable system, the result shows that the method can overcome the
disturbance effect on the precision of model identification. Finally, the coordinated control system modeling of a thermal
power unit is carried out, and simulation results show the method effectiveness. |
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