A State Estimation under Parameter Uncertainty Algorithm based on Interval Global Optimization

This paper presents a software sensor algorithm to estimate states under model uncertainty, which is capable of estima te states of nonlinear process models with parameter uncertainty. The algorithm is based on IMHSE method, which is an optimization- based method that relies on interval analysis, whic h provides it with global convergent properties, and guaranteed o ptimization results. The modified method (IMHSE-PU) results in a strong method which can handle models with parameter uncertainty. The results obtained of estimations on a biotechnologic al process show the benefits of IMHSE-PU.