Improving industrial mpc performance with data-driven disturbance modeling

Industrial model predictive control (MPC) usually assumes a step-like disturbance model, which is insufficient when there is model mismatch in the plant or high order disturbances. In this paper, we demonstrate that a disturbance model identified from close-loop data is desirable for dynamic matrix control (DMC). We introduce a subspace based method to obtain such a model. The method estimates Markov parameters of the disturbance model using closed-loop data along with known input-output model information in the DMC controller. Simulation results are given to compare the proposed approach with traditional DMC.