A decomposition based least squares iterative parameter estimation algorithm for controlled autoregressive autoregressive moving average systems

This paper presents a decomposition based least squares (i.e., two-stage least squares) iterative parameter estimation algorithm for controlled autoregressive autoregressive moving average systems by combining the iterative technique and the decomposition technique. After decomposing the identification model, we obtain two subsystems, one including the parameters of the system model, and the other including the parameters of the noise model. The proposed least squares based iterative algorithm has a high computational efficiency because the dimensions of the involved covariance matrices in each subsystem become small. The simulation example is provided.