Multivariable Model State Feedback

Abstract A Model State Feedback (MSF) implementation of IMC controllers for multivariable processes is developed. Model State Feedback uses a linear combination of past and present process model states to form the IMC control efforts. Implementing the IMC control in model state feedback form compensates for past control effort saturation, which can lead to significant performance degradation in lead -lag IMC control implementations. Another potential source of performance degradation is the distortion of the control vector which occurs when components of the control vector are truncated due to control effort saturation. Saturation induced directionality problems are avoided by temporarily increasing filter time constants to bring the control vector into the constraint set. The required computations are straight forward because control efforts are available as explicit functions of filter constants. A defect of MSF implementations is that they can exhibit limit cycles, or large overshoots, for very small filter constants. A simple method for estimating the minimum filter time constants which do not lead to poor performance for SISO systems is presented. The method is an extension of that developed by Campo and Morari(1990).