Abstract
The identification difficulties for a dual-rate Hammerstein system lie in two aspects. First, the identification model of the system contains the products of the parameters of the nonlinear block and the linear block, and a standard least squares method cannot be directly applied to the model; second, the traditional single-rate discrete-time Hammerstein model cannot be used as the identification model for the dual-rate sampled system. In order to solve these problems, by combining the polynomial transformation technique with the key variable separation technique, this paper converts the Hammerstein system into a dual-rate linear regression model about all parameters (linear-in-parameter model) and proposes a recursive least squares algorithm to estimate the parameters of the dual-rate system. The simulation results verify the effectiveness of the proposed algorithm.
Highlights
A traditional discrete-time system is called a single-rate system, in which the input refreshing period equals the output sampling period [1,2]; In some complex nonlinear systems, the sampling rates of the output and the input are different due to the limitation of the measurement technology and method
The intent of this paper is to study identification methods of Hammerstein nonlinear systems with dual-rate sampling period in input-output signals
We transform the Hammerstein system in Equations (1) and (2) into a dual-rate linear-in-parameter identification model, which is suitable for the dual-rate sampled data, by using the polynomial transformation technique [20] and the key variable separation technique [12]
Summary
A traditional discrete-time system is called a single-rate system, in which the input refreshing period equals the output sampling period [1,2]; In some complex nonlinear systems, the sampling rates of the output and the input are different due to the limitation of the measurement technology and method. The traditional single-rate discrete-time model is not suitable for the dual-rate sampled-data of the Hammerstein system These bring difficulties to directly using a standard least squares method. The intent of this paper is to study identification methods of Hammerstein nonlinear systems with dual-rate sampling period in input-output signals. The polynomial transformation technique [20] with the key variable separation technique [12], a dual-rate linear-in-parameter identification model for the dual-rate sampled Hammerstein system is derived, which is suitable for the dual-rate sampled-data, and it is easy to use the standard least squares method to identify the system parameters.
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