In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typica...In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system.展开更多
针对受非零均值高斯噪声干扰的双率Hammerstein输出误差系统,提出一种基于偏差补偿的递推最小二乘(bias compensation based recursive least squares,BCRLS)辨识算法。首先,利用多项式变换技术将目标系统转换为可直接采用双率采样数据...针对受非零均值高斯噪声干扰的双率Hammerstein输出误差系统,提出一种基于偏差补偿的递推最小二乘(bias compensation based recursive least squares,BCRLS)辨识算法。首先,利用多项式变换技术将目标系统转换为可直接采用双率采样数据进行辨识的模型,并利用递推最小二乘(recursive least squares,RLS)算法进行辨识。其次,为了对RLS算法给出的有偏参数估计进行有效补偿,在偏差补偿原理的基础上,通过引入非奇异矩阵和扩展信息向量求解偏差项中的参数,推导得到BCRLS辨识算法。最后,通过数值仿真实验表明,BCRLS算法能够获得双率Hammerstein输出误差系统的无偏参数估计;且具有较强的鲁棒性,其辨识精度不容易受到噪声均值和方差变化的影响。展开更多
利用Hammerstein模型对超磁致伸缩作动器(Giant magnetostrictive actuators,GMA)的率相关迟滞非线性进行建模,分别以改进的Prandtl-Ishlinskii(Modified Prandtl-Ishlinskii)模型和外因输入自回归模型(Autoregressive model with exoge...利用Hammerstein模型对超磁致伸缩作动器(Giant magnetostrictive actuators,GMA)的率相关迟滞非线性进行建模,分别以改进的Prandtl-Ishlinskii(Modified Prandtl-Ishlinskii)模型和外因输入自回归模型(Autoregressive model with exogenous input,ARX)代表Hammerstein模型中的静态非线性部分和线性动态部分,并给出了模型的辨识方法.此模型能在1～100Hz频率范围内较好地描述GMA的率相关迟滞非线性.提出了带有逆补偿器和H∞鲁棒控制器的二自由度跟踪控制策略,实时跟踪控制实验结果证明了所提策略的有效性.展开更多
基金supported in part by the National Natural Science Foundation of China(62373070 and 52272388)in part by the Chongqing Natural Science Foundation(CSTB2024NSCQ-QCXMX0054,CSTB2022NSCQ-MSX1225 and CSTC2024YCJH-BGZXM0042)in part by the Key Research and Development Project of Anhui Province(202304a05020060).
文摘In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system.
文摘针对传统的线性模型不足以描述分解炉复杂系统的问题,结合垃圾协同处置的背景,研究了一种基于极限学习机(extreme learning machine,ELM)的MISO Hammerstein-Wiener(multiple-input single-output Hammerstein-Wiener)模型分解炉温度建模及预测控制方法,用以实现分解炉温度的稳定控制。模型以喂煤量和垃圾衍生燃料流量(refuse derived fuel,RDF)为输入、分解炉温度为输出,并且采用ELM拟合非线性环节,ARMAX(autoregressive moving average with extra input)模型来描述动态线性环节,递推最小二乘法辨识出模型混合参数,奇异值分解得到模型的参数估计。分解炉控制方法采用两步法预测控制。首先,建立非线性环节逆模型;其次,采用广义预测控制算法得到中间变量;最后,中间变量经过非线性环节逆模型输出得到模型的控制量。仿真实验表明,ELM的引入提高了模型的拟合精度。与传统的预测控制相比,所提的控制方法稳定性更强、跟随性更好。
文摘针对受非零均值高斯噪声干扰的双率Hammerstein输出误差系统,提出一种基于偏差补偿的递推最小二乘(bias compensation based recursive least squares,BCRLS)辨识算法。首先,利用多项式变换技术将目标系统转换为可直接采用双率采样数据进行辨识的模型,并利用递推最小二乘(recursive least squares,RLS)算法进行辨识。其次,为了对RLS算法给出的有偏参数估计进行有效补偿,在偏差补偿原理的基础上,通过引入非奇异矩阵和扩展信息向量求解偏差项中的参数,推导得到BCRLS辨识算法。最后,通过数值仿真实验表明,BCRLS算法能够获得双率Hammerstein输出误差系统的无偏参数估计;且具有较强的鲁棒性,其辨识精度不容易受到噪声均值和方差变化的影响。
文摘利用Hammerstein模型对超磁致伸缩作动器(Giant magnetostrictive actuators,GMA)的率相关迟滞非线性进行建模,分别以改进的Prandtl-Ishlinskii(Modified Prandtl-Ishlinskii)模型和外因输入自回归模型(Autoregressive model with exogenous input,ARX)代表Hammerstein模型中的静态非线性部分和线性动态部分,并给出了模型的辨识方法.此模型能在1～100Hz频率范围内较好地描述GMA的率相关迟滞非线性.提出了带有逆补偿器和H∞鲁棒控制器的二自由度跟踪控制策略,实时跟踪控制实验结果证明了所提策略的有效性.
