Model-on-Demand Estimation for Improved Identification and Control of Process Systems

Friday, December 6, 2002 - 9:35am - 10:35am
Keller 3-180
Daniel Rivera (Arizona State University)
In recent years we have been pursuing the concept of nonlinear identification and control through a data-driven framework named Model-on-Demand (MoD). The MoD approach enhances traditional local modeling and provides the potential for performance rivaling global methods (such as NARX models and neural networks) while involving substantially less detailed knowledge of model structure from the user and much more reliable numerical computations.

Research in our laboratory (performed in collaboration with the Division of Automatic Control at Linkoping University, Sweden) has focused on demonstrating the MoD estimation framework as an effective, practical means for modeling and controlling nonlinear process systems. Research topics have included such diverse problems as MoD-based automated smoothing of empirical transfer function estimates (ETFEs), systematic design of databases for MoD estimation using multi-level pseudo-random and minimum crest factor multisine input signals, and the development of a comprehensive MoD-based Predictive Control methodology. A Matlab-based tool for MoD estimation and control, developed in our laboratory in collaboration with Linkoping researchers, is available in the public domain.

The presentation will describe our general experiences with MoD estimation in each of these topical areas. Some pressing challenges and open issues in the application of MoD estimation will be discussed. The talk will conclude with a summary of current activities, among these the application of MoD-based estimation and control to inventory management in supply chains.