Linear parameter-varying systems identification with slowly sampled outputs subjected to unknown time-delays and outliers. (January 2019)

• Record Type: Journal Article
• Authors: Liu, Xin; Yang, Xianqiang; Guo, Feng
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 1(2019)
• Page Start: 5
• Page End: 17
• Publication Date: 2019-01
• Subjects: Robust global approach -- linear parameter-varying model identification -- random output time-delays -- Laplace distribution -- expectation-maximization algorithm; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651818760937
• Languages: English
• ISSNs: 0959-6518

Abstract

This article proposes a robust global identification approach for nonlinear systems with slowly sampled outputs subjected to unknown time-delays. The inputs of the system are fast-rate sampled while the informative outputs are uniformly sampled at a slow rate. In practical industries, the outlier and output time-delays are commonly encountered and they are both considered in this work. To eliminate the impact brought by the outliers, the robust linear parameter-varying finite impulse response observation model based on the Laplace distribution is established. The unknown time-delay is treated as a random integer at each sampling moment which follows the uniform distribution with the boundary known a priori. The proposed robust approach is derived in the expectation-maximization algorithm scheme and the formulas to iteratively update the model parameters, the scale parameter and the random output time-delays are derived, respectively. The unmeasurable noise-free fast-rate sampled outputs are estimated by simulating the identified model. A numerical example, the mass-spring-damper system and the two-link robotic manipulator are utilized to verify the effectiveness of the proposed approach.