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非线性离散时间系统的自适应函数观测器 总被引:2,自引:0,他引:2
针对一大类非线性离散时间系统提出了一种自适应函数观测器(AFO)。通过引入状态变换,得到了一类降阶形式的状态估计问题。采用一种稍加修改的强跟踪滤波算法估计降阶状态向量,然后利用降阶状态向量估计非线性状态函数。给出了AFO局部渐近收敛的充分条件。数值仿真示例显示AFO是一种具有强跟踪性质的自适应观测器,能在估计非线性状态函数的同时准确估计未知时变参数。 相似文献
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高/低通复合多通道系统辨识的测试信号设计 总被引:1,自引:1,他引:0
从频域的角度研究了二进制多频序列(Muhifrequency Binary Sigal).从该信号的基本特点入手,通过对信号变化疏密的设计,调整信号的频谱分布.当已知待测系统的频谱集中在某些频段时,选取适当的二进制多频序列信号,能使测试信号的频谱也集中在那些频段,能充分地利用测试信号.大大提高了信噪比.设计出高频和低频复合的二进制多频序列信号.应用于一个通道是高通的,另一个通道是低通的二输入系统,能同时充分激励2个通道,取得较好的辩识效果.仿真算例可以看出有效性. 相似文献
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Dynamic principal component analysis (DPCA) is an extension of conventional principal component analysis (PCA) for dealing with multivariate dynamic data serially correlated in time. Based on the fact that the measured variables in relation to chunk monitoring of the industrial fluidized-bed reactor are highly cross-correlated and auto-correlated, this paper presents a practical strategy for chunk monitoring by adopting DPCA in order to overcome the shortcomings of the conventional method. After introducing the basic principle of DPCA, both how to determine the time lagged length of data matrix and how to calculate the nonparametric control limits when the dynamic data are not subject to the assumption of independently identically distribution (IID) were discussed. An appropriate DPCA model based on the real data from a industrial fluidized-bed reactor was built, with parallel analysis and empirical reference distribution (ERD) method to select time lagged length and control limits, respectively. During data pretreatment, data smoothing was used to reduce noise and the serial correlations to some degree. The simulation test results showed the effectiveness of the DPCA based method. 相似文献
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