239d Model Reduction, Estimation and Control of Multiscale Systems

Vinay Prasad Multiscale systems offer unique challenges in modeling and control. From a modeling viewpoint, these systems are of very high dimension. Most of the systems have a stochastic component, resulting in noisy outputs. Additionally, their models are usually not in standard state space form, meaning that the application of advanced control strategies is not straightforward. The small number of measurements available also means that observability is an important issue in estimation and model identification in these systems. From a system analyst's viewpoint, another important characteristic is that the variables we wish to control (surface roughness, for example) are typically microscale quantities, while the possible manipulated variables (temperature, pressure, flows) act at the macroscale. This means that the model identification and estimation at the microscale cannot be neglected in developing a control strategy.