| Multivariable control has been a challenging research area in process control, particularly for dynamically coupled and nonlinear time varying process systems. Since 1960, various multivariable control techniques have been proposed in the literature to address these issues. Out of these techniques Model Predictive Control (MPC) based control methodologies has received considerable attention during last few decades.; The aim of this thesis is to provide a comprehensive analysis of different MPC techniques that can be used for a wider class of multivariable process systems. MPC schemes use a model to predict the future behavior of the process to be controlled and the control move that provides the minimum future error is chosen to drive the system. The model employed in the MPC scheme is generally a linear model. The representation of the linear model in two different forms, parametric form or weighting sequence form, has developed two popular and widely accepted MPC techniques, such as Generalized Predictive Control ( GPC) and Dynamic Matrix Control (DMC) based MPC techniques. (Abstract shortened by UMI.)... |
