Neighbouring-Extremal Control for Steady-State Optimization Using Noisy Measurements

Optimal operation of chemical plants is usually accomplished by finding the optimal steady state using the nominal set of disturbances and model parameters. The optimization is in most cases model based and therefore subject to uncertainties. This may lead to sub optimal control actions with significant economical losses. One idea to tackle this problem is to use the available measurements to adapt the inputs during operation in a feedback control scheme. This can be achieved by a neighbouring extremal controller that updates the inputs based on the deviation of the measured outputs from their nominal value. In this paper we generalize the neighbouring extremal control design that has been presented in the literature to explicitly handle measurement noise and implementation errors. The benefits of our method are illustrated in a case study where we show that the sensitivity of the controller performance to measurement noise is considerably reduced.