A robust state estimator design for nonlinear plants

The problem of designing an online estimator to infer the state of observable or detectable multi-input multi-output nonlinear plants with exogenous inputs is addressed. Sufficient conditions for robust convergence with respect to model parameter errors, and a tractable tuning procedure are obtained. The inference of conversion, molecular weight and polydispersity from density, temperature and volume measurements in a free-radical homopolymerization reactor is studied as an application example.