There is a large variety of methods in literature for process design and control, which can be classified into two main categories. The methods in the first category have a sequential approach in which, the control system is designed, only after the details of process design are decided. However, when process design is fixed, there is little room left for improving the control performance. Recognizing the interactions between process design and control, the methods in the second category integrate some control aspects into process design. With the aim of providing an exploration map and identifying the potential areas of further contributions, this paper presents a thematic review of the methods for integration of process design and control. The evolution paths of these methods are described and the advantages and disadvantages of each method are explained. The paper concludes with suggestions for future research activities.

/pdf/integration-of-process-design-and-control-a-review-3kmrbq033k.pdf

Integration of process design and control: A review

http://www.ece.ualberta.ca/~marquez/journal_publications_files/papers/tan_cce04.pdf

Multimodel analysis and controller design for nonlinear processes

The most effective control algorithm for regulation of glucose levels in persons with diabetes is determined using control-relevant nonlinearity analysis of a diabetic system model. Theoretical control-relevant nonlinearity analysis is performed using the Optimal Control Structure and a norm-based nonlinearity measure. These results are correlated with results of controller performance assessment trials, based on optimizing the rejection of glucose disturbances. Performance is quantified using a standard quadratic performance objective as well as an asymmetric performance objective in which negative glucose deviations are penalized more highly than positive deviations because of the greater health concerns associated with negative deviations. The control-relevant nonlinearity assessment indicates that the best controller design is a linear algorithm except when the desired performance is strongly asymmetric. For standard meal disturbances, the system is found to be well regulated using proportional–derivative control or standard linear model predictive control with no significant benefit observed in using nonlinear model-based control. © 2005 American Institute of Chemical Engineers AIChE J, 51: 544–554, 2005

Glucose control design using nonlinearity assessment techniques

This paper introduces a new method for quantifying the nonlinearity of a process. The approach compares the controllability and observability gramians of a system that is linearized at its steady state operating point to empirical gramians, which are computed from data collected within an operating region of the nonlinear process. This comparison results in two measures for the nonlinearity of the input-output behavior of a process: one for the nonlinearity in the input-to-state behavior (controllability) and one for the nonlinearity in the state-to-output behavior (observability). This is important for controller/observer design, as well as model reduction, because it indicates when it is appropriate to replace a nonlinear model with a linearization around its operating point. In addition, severely nonlinear models can be identified as being Wiener-like models, Hammerstein-like models, or nonlinear in both input-to-state and state-to-output behavior. The proposed approach is illustrated with several examples comparing this method with nonlinearity measures used in the literature.

A Gramian Based Approach to Nonlinearity Quantification and Model Classification

Knowledge of how nonlinear a stochastic system is important for many applications. For example, a full-blown nonlinear filter is needed in general if the system is highly nonlinear, but a quasi-linear filter (e.g., an extended Kalman filter) is sufficient if the system is only slightly nonlinear. We first briefly survey various measures of nonlinearity for different representations of problems. Unfortunately, the conclusion of our survey is that a good quantitative measure of nonlinearity for stochastic systems is still lacking and existing measures designed for other applications are not suitable here. In view of this, we propose a general measure of nonlinearity for stochastic systems based on the idea of quantifying its deviation from linearity. It can be interpreted as a measure of the mean-square distance between a point (i.e., the given nonlinear system) and a subspace (i.e., the set of all linear systems) in a functional space. Properties and computation of this measure are explored. A numerical example is given in which the measure is applied to a target tracking problem.

A. Helbig

Papers

Nonlinearity measures: definition, computation and applications