Abstract - Seborg GOALI Model-based process control strategies utilizing nonlinear dynamic models can provide improvements over conventional PID and control strategies based on linear dynamic models. A factor for successful application of nonlinear control systems is the availability of a reasonably accurate, dynamic model. For some industrial control applications, physical models can be developed from first principles such as unsteady-state mass and energy balances. However, it may not be feasible to use a physical model as part of the on-line control calculations due to model complexity, unknown model parameters, or the lack of key measurements. In many applications, accurate physical models are not available for a variety of reasons, which include process complexity, lack of process understanding, limited measurements, and the time and effort required to develop them. For these situations, an attractive alternative is to develop an empirical nonlinear model which is consistent with available a priori physical information using nonlinear identification techniques. In this joint academic-industrial research project, process control researchers at the University of California at Santa Barbara and at DuPont will address a number of issues in nonlinear identification which limit the effectiveness of existing methods and their widespread application. These issues include: (1) selection of the input sequence to ensure appropriate process excitation; (2) incorporation of available a priori process knowledge; (3) selection of key design parameters such as sampling period, amount of data to be acquired, model orders and model time delay; (4) characterization of model uncertainty; and (5) on-line model updating and adaptation. The identification methods resulting from the research will be evaluated in a number of simulation and experimental studies, including industrial applications. A case study will provide a comparison of alternative identification techniques based, in part, on whether they can be used to develop effective model-based control systems.
