Recursive subspace identification with prior information using the constrained least squares approach

• Recursive subspace identification is developed for systems with prior information.
• Constrained least square approach is used to incorporate the prior information leading to a computationally efficient identification approach.
• A simulation indicates that the proposed algorithm can generate a model with improved accuracy.

It is essential to develop high quality models for process control and other applications. The incorporation of prior information in subspace identification has been investigated to obtain improved model quality. One of the recent developments incorporates the prior information using the constrained least squares (CLS). In many online applications, the amount of process data for model identification grows with time, and it is therefore necessary to develop a recursive algorithm for online identification of process models and to address the time-varying characteristics of the systems. In this paper, a recursive subspace identification algorithm incorporating prior information is developed using the constrained recursive least squares (CRLS). It is shown via a simulation example that the state space model identified using the proposed algorithm possesses improved accuracy.