Tuning Parameters for DMC and MPC Controllers

DMC (Dynamic Matrix Control) and MPC (Model Predictive Control) are advanced control strategies widely used in industrial process control. Both controllers require meticulous parameter tuning to balance system response speed with stability.

The core parameter tuning approach typically involves these key steps: Model Accuracy Validation: Ensure the predictive model of the controlled object accurately reflects actual dynamic characteristics - this forms the foundation for all parameter optimization. In implementation, this involves system identification techniques and model validation tests using historical data. Sampling Period Selection: Determined based on process dynamics, typically set to 1/5 to 1/10 of the system's dominant time constant. This can be implemented using frequency analysis methods in code. Optimization Horizon Settings: The prediction horizon (P) should cover the main dynamic response of the process, while the control horizon (M) is usually set to 10%-30% of the prediction horizon. These parameters directly affect the optimization problem size in MPC algorithms. Weight Matrix Adjustment: Output error weights and input change weights require dynamic adjustment based on control objectives. The former affects tracking performance while the latter determines control action aggressiveness. In code implementation, these are typically diagonal matrices in the quadratic cost function.

Practical tuning can employ a hierarchical optimization strategy: first determine basic parameters through open-loop tests, then perform fine-tuning using closed-loop simulations. For complex systems, automated tuning tools or multi-objective optimization with evolutionary algorithms are recommended. Implementation often involves optimization solvers like quadratic programming for real-time control calculations.