Parameter identification for Hammerstein nonlinear system with polynomial and state space model

Abstract

This study investigates a two-stage parameter identification algorithm for the Hammerstein nonlinear system based on special test signals. The studied Hammerstein nonlinear system has a static nonlinear subsystem represented by polynomial basis function and a dynamic linear subsystem described by canonical observable state space model, and special test signals composed of binary signals and random signals are applied to parameter identification separation of the nonlinear subsystem and linear subsystem. The detailed identification procedures consist of two main steps. Firstly, using the characteristics that binary signals do not excite the static nonlinear subsystem, the dynamic linear subsystem parameters are identified through recursive least squares algorithm based on input-output data of binary signals. Secondly, unmeasurable state variables of the identified system are replaced with estimated values, thus the nonlinear subsystem parameters are obtained using recursive least squares algorithm with the help of input-output data of random signals. The efficiency and accuracy of proposed identification scheme are confirmed on experiment results of a numerical simulation and a practical nonlinear process, and experimental simulation results show that the developed two-stage identification algorithm has excellent predictive performance for identifying the Hammerstein nonlinear state space systems.

Estimation of wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

“…Set hyperparameters in the presented method as S 0 = 0.999, ρ = 1.0, λ = 0, and α = 0.25. To certificate the effectiveness of the proposed NFM and its estimate technique, BP neural network, 37 Kalman filtering, 38 and polynomial model 39 are also used for comparison of nonlinear block fitting. Table 3 shows error comparison of nonlinearity fitting based on three modeling methods, where mean squared error ($$$ \mathrm{MSE}=\frac{1}{N}\sum \limits_{t=1}^N{\left({\mathrm{observed}}_t-{\mathrm{predicted}}_t\right)}^2 $$$) and mean absolute error ($$$ \mathrm{MAE}=\frac{1}{N}\sum \limits_{t=1}^N\left(\left|{\mathrm{observed}}_t-{\mathrm{predicted}}_t\right|\right) $$$) are involved.…”

“…Set hyperparameters in the presented method as S 0 = 0.999, 𝜌 = 1.0, 𝜆 = 0, and 𝛼 = 0.25. To certificate the effectiveness of the proposed NFM and its estimate technique, BP neural network, 37 Kalman filtering, 38 and polynomial model 39 are also used for comparison of nonlinear block fitting. Table 3 shows error comparison of nonlinearity fitting based on three modeling methods, where mean squared error…”

Monitoring method of solder layer void damage of IGBT module based on transfer function

“…Some parameters are difficult to obtain, and the aging process is difficult to monitor [27]. In [28][29], according to the relationship between the time constant of the chip junction temperature cooling curve and Cauer model parameters, the Cauer model parameters of IGBT module were directly obtained by fitting the cooling curve. This method needs to obtain multiple sets of equations under different heat dissipation conditions, and the calculation process is complex.…”