For a dual-rate control system where the output sampling interval is an integer multiple of the control interval, we propose a model-based inferential control scheme which uses a fast-rate model to estimate the intersample outputs and then supply them to a controller at the fast rate. Comparing such an inferential controller with the corresponding fast single-rate controller, we conclude that t...

Title of dissertation: INFERENTIAL MODEL PREDICTIVE CONTROL USING STATISTICAL TOOLS Kedar Himanshu Dave, Doctor of Philosophy, 2005 Dissertation directed by: Professor Thomas J. Mc Avoy Department of Chemical Engineering With an ever increasing emphasis on reducing costs and improving quality control, the application of advanced process control in the bulk chemical and petrochemical industry is...

We describe a technique for speeding up inference for model-based abduction tasks that trades off inference time and/or space for the fraction of queries correctly answered. We compile a knowledge base (for which inference may be intractable) into a set of rules that cover the most likely queries using simple criteria that do not entail extensive knowledge engineering effort, such as subset-min...

In this tutorial, we discuss approaches to control of one or more primary variables in a process using secondary measurements. This approach is useful when the primary variable is not easily measured, or has large time delays or lags associated with it. It is also useful when the secondary measurements contain information about disturbances that affect the primary variable. We start by discussi...

In the last years a lot of papers appearing in the control literature introduced elaborated control design algorithms. Most of them contain mathematical approaches that apparently solve control problems. However in many cases the engineering perspective is lost. Some case-dependent conclusions are proposed to be of general use and some properties are considered as universal, even they can fail ...

A successful industrial application of a novel type biomass estimator based on Genetic Programming (GP) is described in the paper. The biomass is inferred from other available measurements via an ensemble of nonlinear functions, generated by GP. The models are selected on the Pareto front of performance-complexity plane. The advantages of the proposed inferential sensor are: direct implementati...