Regulatory control structure selection of linear systems

The selection of manipulated and controlled variables as well as the structure and parameters of their interconnecting controller is an important synthesis problem that has received a lot of attention from the process control community. This paper presents a mathematical programming methodology for the solution of this multifaceted synthesis problem. The method is applicable to linear or linearised nonlinear systems. In addition, a quantitative index is proposed for the estimation of the advantages in using unstructured controllers, such as model predictive controller (MPC, when compared to classical structured controllers such as multivariable proportional-integral-derivative (PID) controllers. Two case studies are presented where the potential of the method as a rigorous technique for solving the regulatory control structure selection problem for linear systems is established. (C) 2002 Elsevier Science Ltd. All rights reserved.