Linear Regression Method for Estimating Approximate Normalized Coprime Plant Factors

Studies on iterative identification and model based control design have shown the necessity for identifying models on the basis of closed-loop data. Estimating models on the basis of closed-loop data requires special attention due to cross correlation of noise and input signals and the possibility to estimate unstable systems operating under a stabilizing closed-loop controller. This paper provides a method to perform an approximate identification of normalized coprime factorization from closed-loop data. During the identification, a constrained linear regression parametrization is used to estimate the normalized coprime factors. A servomechanism case study illustrates the effectiveness of the proposed algorithm.