| 基于自适应遗忘因子RLS算法的稳压器模型在线辨识 |
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| 引用本文： | 钱虹,江诚,潘岳凯,史哲烽.基于自适应遗忘因子RLS算法的稳压器模型在线辨识[J].核动力工程,2019,40(6):124-129. |
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| 作者姓名： | 钱虹 江诚 潘岳凯 史哲烽 |
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| 作者单位： | 上海电力学院自动化工程学院,上海,200090;上海市电站自动化技术重点实验室,上海,200090;上海电力学院自动化工程学院,上海,200090;上海自动化仪表有限公司,上海,200072 |
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| 摘 要： | 为提高稳压器时变系统模型辨识的准确性及其参数在线辨识的快速性和鲁棒性，并研究遗忘因子大小对遗忘因子递推最小二乘法算法特性的影响，提出了一种基于模糊算法的自适应遗忘因子递推最小二乘法算法。该算法以系统动态特性值与辨识模型值之间的残差时间序列平均值及其变化率作为模糊算法的输入，遗忘因子修正量为输出，从而实现遗忘因子的自适应调整。通过对某核电厂稳压器降压系统进行仿真，结果表明，该算法可实时调整遗忘因子大小，有效地解决了稳压器模型参数时变性的问题，得到了较精确的时变模型；有效地解决了参数辨识结果稳定性和收敛速度相互矛盾的问题。因此，该算法具有可行性和优越性。
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| 关 键 词： | 稳压器 自适应遗忘因子 递推最小二乘法 模糊算法 时变模型辨识 |
Online Identification of Regulator Model Based on Adaptive Forgetting Factor RLS Algorithm |
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| Abstract: | In order to improve the accuracy of the time-varying system model identification of the voltage regulator and the rapidity and robustness of the on-line identification of the parameters, and to study the effect of forgetting factor on the performance of forgetting factor recursive least squares algorithm, an adaptive forgetting factor recursive least squares algorithm based on fuzzy algorithm is proposed in this paper. The average value of time series of the residual and its change rate between the dynamic characteristic value of the system and the identified model value are taken as the input of the fuzzy algorithm, and the correction of the forgetting factor is taken as the output to realize the adaptive adjustment of the forgetting factor. The simulation results of a regulator pressure reduction system of a nuclear power plant show that the algorithm can adjust the forgetting factor in real time, and effectively solve the time-varying problem of the parameters of the regulator model. Thus it can obtain a more accurate time-varying model, and can effectively solve the contradiction between the stability and the convergence speed of parameters identification results. Therefore, the algorithm is feasible and superior. |
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