Recursive least squares identification of hybrid Box–Jenkins model structure in open-loop and closed-loop

Abstract

Inspired by the fact that, in order to obtain a global optimal solution, a continuous plant should be identified simultaneously with the noise model, a simple but effective identification method is firstly proposed for hybrid Box–Jenkins structure in open-loop and close-loop. Two recursive generalized extended least squares algorithms are developed for different plant models. In recursive computations, the idea of auxiliary model has been applied to make the global recursive identification possible, and the idea of delay compensation has been introduced to handle the identification of SOPDT plant model effectively. Meanwhile, the online implementation issues of recursive algorithms are discussed. The two proposed algorithms can be further extended to closed-loop systems by an appropriate closed-loop setup. The simulation examples demonstrate the accuracy and effectiveness of the proposed method in open-loop and closed-loop.