Optimal Sensor Location for Nonlinear Dynamic Systems via Empirical Gramians

This paper presents a new approach for determining sensor locations for nonlinear dynamic systems. The method uses empirical observability gramians for observability analysis and combines the information from this investigation with observability measures which have been previously proposed in the literature. This approach offers the advantage over other methods in that it is directly applicable to nonlinear systems without resorting to linearization of the model. The presented procedure has been applied to a binary distillation column model. Additionally, the effect of scaling of a model for sensor placement has been examined as well as the conclusions which can be drawn from different observability measures.