Least Squares Parameter Estimation for Input Nonlinear Controlled Autoregressive Systems Based on the Key Variable Separation

There exists the product of two unknown parameter sets in input nonlinear systems. Thus, the model parameters are unidentifiable. By normalizing the system parameters and using key variable separation, a least squares-based iterative algorithm is proposed to identify input nonlinear controlled autoregressive systems. The proposed algorithm is verified by simulation examples.