LIMITATIONS OF DYNAMIC MATRIX CONTROL

Dynamic matrix control (DMC) is based on two assumptions which limit t he feedback performance of the algorithm. The first assumption is that a stable step response model can be used to represent the plant. The second assumption is that the difference between the measured and the predicted output can be modeled as a step disturbance acting on the ou tput. These assumptions lead to the following limitations: 1. Good per formance may require an excessive number of step response coefficients . 2. Poor performance may be observed for disturbances affecting the p lant inputs. 3. Poor robust performance may be observed for multivaria ble plants with strong interactions. Limitations 1 and 2 apply when th e plant's open-loop time constant is much larger than the desired clos ed-loop time constant. Limitation 3 is caused by gain uncertianty on t he inputs. In this paper we separate the DMC algorithm into a predicto r and an optimizer. This enables us to highlight the DMC limitations a nd to suggest how they can be avoided. We demonstrate that a new model predictive control (MPC) algorithm, which includes an observer, does not suffer from the listed limitations.