Analysis and tuning of a three-term DMC
This work addresses control system design for industrial applications, but it is incremental as it extends an existing MPC framework with an additional term.
The paper tackles the problem of improving Model Predictive Control (MPC) performance by studying a three-term Dynamic Matrix Control (DMC) algorithm, which adds a weighted norm of output increments to the standard two-term quadratic programming formulation, and shows through analysis and simulation that it can achieve higher performance and robustness than the two-term DMC.
Most MPC (Model Predictive Control) algorithms used in industries and studied in the control academia use a two-term QP (quadratic programming), where the first term is the weighted norm of the output errors, and the second term is that of the input increments. In this work, a DMC (Dynamic Matrix Control) algorithm that uses three-term QP is studied, where the third term is the weighted norm of the output increments. In the analysis, a relationship between the three-term DMC and the two-term DMC is established; based on that, the closed-loop response curves are derived. Based on the analysis, two controller tuning procedures are developed for the three-term DMC, one for closed-loop step response and one for disturbance reduction. Finally, it will be proven that the three-term DMC can achieve a higher performance and robustness than the two-term DMC can. Simulation studies are used to demonstrate the findings and the tuning methods.