Improved predictions from measured disturbances in linear model predictive control

Abstract

Measured disturbances are often included in model predictive control (MPC) formulations to obtain better predictions of the future behavior of the controlled system, and thus improve the control performance. In the prediction model, a measured disturbance is in many ways treated like a control input to the system. However, while control inputs change only once per sampling interval as new control inputs are calculated, measured disturbances are typically sampled from continuous variables. While this difference is usually neglected, it is shown in this paper that taking this difference into account may improve the control performance. This is demonstrated through two simulation studies, including a realistic multivariable control problem from the petroleum industry. The proposed method requires only a minor modification in the implementation of the prediction model, and may thus improve the control performance with a minimal effort.