Subspace Identification For Mimo Systems In The Presence Of Trends And Outliers

In this paper we present a framework for subspace identification of multiple-input multiple-output linear time-invariant systems from data corrupted by outliers and piece-wise linear trends. The subspace identification problem is formulated as a sparsity constrained rank minimization problem that is relaxed using the nuclear norm and the l(1)-norm. The proposed identification method has been validated on a simulated example and on a case study using data from a pilot-plant distillation column.