State-space interpretation of model predictive control

A model predictive control technique based on a step response model is developed using state estimation techniques. The standard step response model is extended so that integrating systems can be treated within the same framework. Based on the modified step response model, it is shown how the state estimation techniques from stochastic optimal control can be used to construct the optimal prediction vector without introducing significant additional numerical complexity. In the case of integrated or double integrated white noise disturbances filtered through general first-order dynamics and white measurement noise, the optirnal filter gain is parametrized explicitly *Department of Chemical Engineering, Auburn University, Auburn AL 36849-5127 phone (205)844-2060, fax (205)844-2063, e-mail [email protected] TO whom all correspondence should be addressed: phone (818)356-4186, fax (818)568-8743, e-mail [email protected] in terms of a single parameter between 0 and 1, thus removing the requirement for solving a Riccati equation and equipping the control system with useful on-line tuning parameters. Parallels are drawn to the existing MPC techniques such as Dynamic Matrix Control (DMC), Internal Model Control (IMC) and Generalized Predictive Control (GPC) .