Data-Driven Output Feedback based Analysis and Control for Unknown Discrete-Time Linear System

What the math gives to ML

The paper provides a constructive way to turn output-only interaction into a finite-dimensional recurrent state by stacking delayed inputs and outputs in a companion-form system. Its transferable asset is not the linear-controller result itself, but the separation between a fixed shift-register memory, known input/output injection, and a learned readout into a control-relevant latent state. This suggests a lightweight recurrent or state-space front end for neural controllers that can replace attention over recent trajectories while preserving explicit memory dynamics. The strongest initial test is a learned observer-memory module for partially observed control and model-based reinforcement learning, comparing it with GRU/LSTM and short-context attention at equal recurrent-state size.