Model predictive control of noisy plants using Kalman predictor and filter

In this paper, an optimal model predictive control (MPC) method is proposed for a class of high order plants which are modeled by the first-order linear systems with time-delay subject to random disturbance. First, the mathematical model of the plant is described by the CARIMA model with colored process noise. An estimation method is used to identify the parameters of noise which is defined as MA Model. Secondly, based on the state space model the optimal MPC is obtained incorporated with Kalman predictor/filter to get the present and future state estimates. A simulation example is given for comparing our method with the previous predictive control method.