Tuning Parameters for DMC and MPC Controllers: Calculation Methods and Implementation

To calculate tuning parameters for DMC (Dynamic Matrix Control) and MPC (Model Predictive Control) controllers, you need to define the desired closed-loop response characteristics of your control system. This process requires careful analysis of the process dynamics and specific performance requirements for the closed-loop system. Key implementation considerations include formulating the dynamic matrix from step response data for DMC or developing the prediction model for MPC. Once the desired closed-loop behavior is specified, you can employ established tuning methodologies such as the Internal Model Control (IMC) approach, which involves matching the controller response to an internal process model, or the classical Ziegler-Nichols method for determining initial parameter estimates. In practical implementation, tuning parameters typically include prediction horizons, control horizons, and weighting factors in the cost function. These parameters can be optimized through iterative simulation and performance evaluation, often using MATLAB's MPC Toolbox functions like mpcsim for closed-loop simulation or nlmpcmove for nonlinear MPC implementations. The tuning process involves adjusting these parameters to minimize objective functions that balance setpoint tracking, control effort, and constraint handling, ultimately achieving the desired control system performance.