Using first-principle models in control

Technical advances in computers make it feasible to use nonlinear process models for automatic control. One advantage over linear model-based controllers is that nonlinear controllers are functional over a wider operating range without retuning; and, where process nonlinearity is the major control problem, nonlinear controllers have demonstrated industrial success. Where designed with adaptive models, tracking phenomenologically meaningful model coefficients for process diagnosis, and using the model for supervisory optimization are other advantages. Disadvantages are the required case-by-case controller design and inability to mathematically guarantee such features as convergence, stability, etc.