Efficient low-order system identification from low-quality step response data with rank-constrained optimization

Abstract

In the presence of low-quality industrial process data, generic step response identification methods typically show unsatisfactory performance and heavily rely on manual intervention of technical personnel. This erects obvious obstacles for the advancement of intelligent manufacturing in process industries. To address these challenges, we propose a novel rank-constrained optimization approach to low-order system identification from step response data, which yields much more accurate and robust estimates than existing modeling methods. By exploiting the inherent low-rank structure of the Hankel matrix of ideal step response, parameters of a low-order process can be accurately recovered by solving a rank-constrained program, which effectively bypasses the two-step procedure in some state-of-the-art algorithms involving significant error accumulation. The alternating direction method of multipliers is adopted to effectively solve the nonconvex error minimization problem and circumvent poor local optima. Case studies on both numerical examples and industrial datasets demonstrate that, the proposed method not only gives much better modeling accuracy, but also secures reliable and robust estimates even for raw low-quality industrial data. This is particularly helpful for automated execution of the identification routine without human intervention, with success percentage over 99% that is remarkably higher than the state-of-the-art.