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Linear parameter-varying systems identification with slowly sampled outputs subjected to unknown time-delays and outliers

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.