【作者】 王建宏； 王道波； 王志胜；

【Author】 WANG Jian-hong,WANG Dao-bo,WANG Zhi-sheng (College of Automation Engineer,Nanjing university of Aeronautics and Astronautics,Nanjing Jiangsu 210016 China)

【机构】 南京航空航天大学自动化学院；

【摘要】 在输入-输出数据中带有噪声时,最小二乘辨识算法给出模型参数的有偏估计。当噪声方差的估计值可获得时,采用偏差补偿最小二乘算法(CLS)能够用来得到模型参数的一致性估计。本文在偏差补偿最小二乘算法的基础上进行推广得到非线性可分离的最小二乘算法(NSLS)。采用适用于噪声环境的非线性可分离的最小二乘算法可准确地辨识飞行器的颤振模态参数,该算法结合传递函数模型,将带噪声系统的辨识问题转化为非线性可分离的最小二乘问题。利用该算法,两噪声的方差值和传递函数中的模型参数可分离地估计出来。最后利用试飞试验数据辨识飞行器的系统参数,验证了该方法的有效性。更多还原

【Abstract】 The least-squares identification method generally gives biased parameter estimates when the observed input-output data are corrupted with noise.If estimates of the noise variances are available,then the principle of biased compensated least-squares (CLS) can readily be used to obtain consistent estimates.In this paper,we extend the CLS method to get the nonlinear separable least squares(NSLS).The nonlinear separable least square algorithm is adopted for aircraft flutter modal parameter identification under noisy environment.Combing with a rational transfer function model,the identification of system with noisy data is transformed into a nonlinear separable least square problem. Using this algorithm,the noise variance parameters and the model parameters can be obtained separately.The simulation with real flight test data shows the efficiency of the algorithm.更多还原