Finite-sample analysis of identification of switched linear systems with arbitrary or restricted switching
This work addresses the challenge of system identification in control theory, particularly for switched systems, and is incremental as it builds on existing least-squares methods with new finite-sample error bounds.
The paper tackles the problem of identifying switched linear systems with measured switching signals by analyzing how switching strategies affect estimation error, showing that error bounds are logarithmic for stable modes but can increase linearly with switching parameters when unstable modes are present.
For the identification of switched systems with a measured switching signal, this work aims to analyze the effect of switching strategies on the estimation error. The data for identification is assumed to be collected from globally asymptotically or marginally stable switched systems under switches that are arbitrary or subject to an average dwell time constraint. Then the switched system is estimated by the least-squares (LS) estimator. To capture the effect of the parameters of the switching strategies on the LS estimation error, finite-sample error bounds are developed in this work. The obtained error bounds show that the estimation error is logarithmic of the switching parameters when there are only stable modes; however, when there are unstable modes, the estimation error bound can increase linearly as the switching parameter changes. This suggests that in the presence of unstable modes, the switching strategy should be properly designed to avoid the significant increase of the estimation error.