A State and Output Sensitivity Controllability Approach for Structural Identifiability of Linear State Space Models

In this paper structural identifiability of state space models, possibly nonlinear in parameters, is assessed by analyzing the controllability of the output sensitivities. Sensitivity analysis provides a mathematical setting to analyze parameter identifiability from a physically intuitive perspective. Both SISO and MIMO cases are treated; in the former case the output controllability matrix rank directly allows to draw conclusions on the model structural identifiability. In the latter case, the analysis requires special attention due to the ordering induced by the vector derivative. The approach is illustrated on a linear compartmental model