PhD

dissertationLinear model predictive control has been widely accepted in industry as an important tool for the operation of difficult interacting processes. Linear identification and control techniques are well developed and well understood. In the industry, it is rare to find a system that is truly linear. While for many systems linear modeling and control can approximate their performance in certain regions, there exist some systems whose nonlinearity is great enough that an approximate linear model and control scheme cannot yield the desired accuracy. In order to control these more complex nonlinear systems, significant research has been dedicated to extending model predictive control to nonlinear systems