Full-order observer for large-scale systems applications to industrial process

Observers are computational methods designed to estimate unmeasured state variables due to the lack of their unavailability for measurement or the excess number of required sensors in a plant. In this work, a full-order observer is proposed to construct the current state of large-scale systems. A high number of state variables characterizes these systems. Without any reduction of state-space model of the plant, we propose an approach based on a transformation state-space model of the plant in order to form two decoupled subsystems. It is shown under the condition of observability of the first subsystem, and stability of the second subsystem, it is possible to build a full-order state observer with a partially fixed dynamic. As applications of this method, we propose two industrial process: a binary distillation column and a Partial Differential Equations system of parabolic type as large-scale systems contain a high number of state variables. Some simulation results are presented to show the feasibility of the proposed approach.