For a dual-rate sampled Hammerstein controlled autoregressive moving average (CARMA) system, thispaper uses the polynomial transformation technology to obtain its dual-rate bilinear identification model whichis suitable for the available dual-rate sampled-data, uses the maximum likelihood principle to construct a unifiedparameter vector of all parameters and an information vector formed by the derivative of the noise variable tothe unified parameter vector, and directly identifies the parameters of the linear block and the nonlinear blockfor the dual-rate Hammerstein CARMA system. The unified parameter vector contains the minimum number ofthe unknown parameters, and the proposed maximum likelihood estimation algorithm has higher computationalefficiency than the over-parameterization model based least squares algorithm.
For a dual-rate sampled Hammerstein controlled autoregressive moving average (CARMA) system, thispaper uses the polynomial transformation technology to obtain its dual-rate bilinear identification model whichis suitable for the available dual-rate sampled-data, uses the maximum likelihood principle to construct a unifiedparameter vector of all parameters and an information vector formed by the derivative of the noise variable tothe unified parameter vector, and directly identifies the parameters of the linear block and the nonlinear blockfor the dual-rate Hammerstein CARMA system. The unified parameter vector contains the minimum number ofthe unknown parameters, and the proposed maximum likelihood estimation algorithm has higher computationalefficiency than the over-parameterization model based least squares algorithm.