Linear offset-free model predictive control: A minimum-variance unbiased filter based approach

摘要

The problem of linear offset-free model predictive control (MPC) with a minimum-variance unbiased (MVU) filter is addressed in this paper. Apart from traditional Kalman filter based approaches, a MVU filter is used in the design of a MPC system. In this framework, the disturbance is allowed to have arbitrary statistics. The MVU filter has the advantage of quick transient estimation behavior, and zero offset output estimation can be obtained for every sample time. We show that the choice of the disturbance model will not affect the estimated output as long as the rank condition is satisfied.