Proximal-based recursive implementation for model-free data-driven fault diagnosis

We present a novel problem formulation for model-free data-driven fault diagnosis, in which possible faults are diagnosed simultaneously to identifying the linear time-invariant system. This problem is practically relevant for systems whose model cannot be identified reliably prior to diagnosing possible faults, for instance when operating...

Challenges in simulating advanced control methods for AO

This paper discusses various practical problems arising in the design and simulation of predictive control methods for adaptive optics. Although there has been increased attention towards optimal prediction and control methods for AO systems, they are often tested in simplified simulation environments. The use of advanced AO simulators...

Input selection in N2SID using group lasso regularization

Input selection is an important and oftentimes difficult challenge in system identification. In order to achieve less complex models, irrelevant inputs should be methodically and correctly discarded before or under the estimation process. In this paper we introduce a novel method of input selection that is carried out as a natural extension in a...