Performance requirements in precision motion systems, including those used in IC manufacturing and printing systems, are ever increasing. For instance, internal deformations cannot be neglected anymore. As a result, measured signals at sensor locations cannot be used directly to evaluate performance at the point of interest. The aim of this paper is to develop an inferential motion control fram...

In many physical systems, the variables that define the performance cannot be measured directly. This may for instance stem from constraints on sensor cost, or from physical limitations in sensor placement. The performance has to be inferred from the measured variables with models, and is therefore inherently determined by model accuracy, see [1] for inferential control in the process industry,...

A new inferential 2-step multiple input/multiple output (MIMO) model predictive control (MPC) of the particle size distribution (PSD) in emulsion polymerization processes is proposed. The bulk-like model describing the PSD is used with the material balances of initiator, radicals, monomer and surfactant. The inferential 2-step control strategy uses two measurements available online (without del...

In the present work a novel inferential control strategy cascaded with a nonlinear profile position controller is employed to control the top and bottom product compositions of a simulated depropanizer column. The inferential model for estimating product compositions is developed using the wave propagation model, and the composition profile position of both rectifying and stripping section is c...

Multivariable inferential feedback control of distillation compositions using principal component regression (PCR) models is presented in this paper. Both static and dynamic models are studied. PCR model based software sensors are developed from process operational data, so that the top and bottom product compositions can be estimated from multitray temperature measurements. The problems of col...