| 引用本文: | 卢行远,侯忠生.基于改进卡尔曼滤波器的扰动抑制无模型自适应控制方案[J].控制理论与应用,2022,39(7):1211~1218.[点击复制] |
| LU Xing-yuan,HOU Zhong-sheng.Model free adaptive control with disturbance rejection based on modified Kalman filter[J].Control Theory and Technology,2022,39(7):1211~1218.[点击复制] |
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| 基于改进卡尔曼滤波器的扰动抑制无模型自适应控制方案 |
| Model free adaptive control with disturbance rejection based on modified Kalman filter |
| 摘要点击 1725 全文点击 574 投稿时间：2021-04-26 修订日期：2022-06-24 |
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| DOI编号 10.7641/CTA.2022.10355 |
| 2022,39(7):1211-1218 |
| 中文关键词 无模型自适应控制 扰动抑制 最小方差估计 卡尔曼滤波 |
| 英文关键词 model free adaptive control disturbance rejection minimum variance estimation Kalman filter |
| 基金项目 国家自然科学基金项目(61833001, 62073025)资助 |
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| 中文摘要 |
| 针对无模型自适应控制方法在测量扰动作用下控制效果不佳的问题, 本文提出了一种新的扰动抑制无模
型自适应控制方案. 首先基于受控系统的动态线性化数据模型及测量扰动的统计特性, 在最小方差估计准则下推导
了基于系统输入输出数据的改进卡尔曼滤波器. 然后基于此滤波器给出了一种新的扰动抑制无模型自适应控制方
案. 该方案仅需用到受控系统的输入输出数据, 即可实现在强测量扰动作用下系统的无模型自适应控制. 仿真结果
显示, 相比现有的扰动抑制无模型自适应控制方案, 该方案在系统跟踪常值参考信号、时变参考信号时均能有效地
抑制测量扰动, 适用性更好的同时可以获得更小的跟踪误差及更大的数据信噪比. |
| 英文摘要 |
| For the problem that the model free adaptive control method has poor control performance under the influence
of measurement disturbance. In this paper, a new model free adaptive control scheme with disturbance rejection is proposed.
Firstly, based on the dynamic linearized data model of the controlled system and the statistical characteristics of measurement
disturbance, an improved Kalman filter based on the system input and output data is derived under the minimum
variance estimation criterion. Then a new model free adaptive control scheme with disturbance rejection is proposed. This
scheme can realize the model free adaptive control of the system under the strong measurement disturbance only by using
the input and output data of the controlled system. The simulation results show that, compared with the existing disturbance
rejection model free adaptive control scheme, the new scheme can effectively suppress the measurement disturbance when
tracking the constant reference signal and the time-varying reference signal, and can obtain the smaller tracking error and
the greater data signal-to-noise ratio while it is more applicable. |