Multi-innovation gradient parameter estimation algorithms for closed-loop Hammerstein nonlinear systems

2017 ◽  
Author(s):  
Ling Xu ◽  
Bingbing Shen ◽  
Feng Ding ◽  
Dongqing Wang
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Closed Loop ◽  
Estimation Algorithms ◽  
Gradient Parameter

scholarly journals Least-Squares Parameter Estimation Algorithm for a Class of Input Nonlinear Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Weili Xiong ◽  
Wei Fan ◽  
Rui Ding
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Least Squares ◽  
Moving Average ◽  
Estimation Algorithm ◽  
Autoregressive Moving Average ◽  
Parameter Estimates ◽  
Persistent Excitation ◽  
Estimation Algorithms ◽  
Parameter Estimation Algorithm

This paper studies least-squares parameter estimation algorithms for input nonlinear systems, including the input nonlinear controlled autoregressive (IN-CAR) model and the input nonlinear controlled autoregressive autoregressive moving average (IN-CARARMA) model. The basic idea is to obtain linear-in-parameters models by overparameterizing such nonlinear systems and to use the least-squares algorithm to estimate the unknown parameter vectors. It is proved that the parameter estimates consistently converge to their true values under the persistent excitation condition. A simulation example is provided.

Adaptive filtering parameter estimation algorithms for Hammerstein nonlinear systems

2016 ◽  
Vol 128 ◽  
pp. 417-425 ◽  
Author(s):  
Yawen Mao ◽  
Feng Ding ◽  
Ahmed Alsaedi ◽  
Tasawar Hayat
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Adaptive Filtering ◽  
Estimation Algorithms

scholarly journals Combined Parameter and State Estimation Algorithms for Multivariable Nonlinear Systems Using MIMO Wiener Models

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Houda Salhi ◽  
Samira Kamoun
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
State Estimation ◽  
Internal Variables ◽  
Decomposition Technique ◽  
Parameter Estimation Problem ◽  
Filter Theory ◽  
Estimation Algorithms ◽  
Wiener Models ◽  
Multivariable Nonlinear Systems

This paper deals with the parameter estimation problem for multivariable nonlinear systems described by MIMO state-space Wiener models. Recursive parameters and state estimation algorithms are presented using the least squares technique, the adjustable model, and the Kalman filter theory. The basic idea is to estimate jointly the parameters, the state vector, and the internal variables of MIMO Wiener models based on a specific decomposition technique to extract the internal vector and avoid problems related to invertibility assumption. The effectiveness of the proposed algorithms is shown by an illustrative simulation example.

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