DMC Simulation Source Code Based on Current Values, Predictions, and Different Control Rates

DMC (Dynamic Matrix Control) is an advanced control algorithm widely used in industrial process control. Its core principle involves optimizing future control actions based on current system output values, prediction models, and predefined control rates to enable fast and stable tracking of setpoints.

The implementation of DMC simulation typically involves the following key steps with corresponding code considerations:

Model Establishment: DMC relies on a dynamic matrix model constructed from the system's step response or impulse response. Through discretization processing, the system's dynamic characteristics are converted into matrix form for subsequent prediction and control calculations. Code implementation typically involves storing step response coefficients in array structures and constructing the dynamic matrix using MATLAB's matrix operations.

Prediction Calculation: Based on current system output values and the dynamic matrix model, the DMC algorithm predicts system behavior over a future time horizon. Prediction accuracy directly affects control performance, requiring appropriate selection of prediction horizon length. Implementation requires matrix multiplication operations between the dynamic matrix and control increment vector, often optimized using vectorized computations for efficiency.

Optimization Control Rate: Different control rates (such as proportional control, optimal control) significantly impact system response speed and stability. In DMC simulation, various control strategies can be simulated by adjusting optimization weight parameters or modifying the control law calculation method. The code typically implements quadratic optimization solvers using functions like quadprog() with weighting matrices for output errors and control increments.

Simulation Analysis: Through simulation programs, dynamic responses under different control rates can be visually observed, including performance indicators like overshoot and settling time. Simulation graphics typically include output curves and control signal curves, facilitating analysis of control algorithm performance. Implementation involves plotting functions with proper axis labeling and real-time data visualization techniques.

Parameter Tuning: The effectiveness of DMC algorithms depends on appropriate selection of model parameters and optimization parameters. Simulation experiments help engineers quickly test different parameter combinations to identify optimal control strategies. Code implementation often includes parameter sweep functionality and automated performance metric calculations for comparative analysis.

DMC simulation programs hold significant practical importance, particularly in complex industrial processes such as chemical and power industries. By adjusting control rates and prediction models, system dynamic performance can be optimized to improve robustness and response speed. Simulation graphics provide intuitive analysis tools that help understand algorithm performance across different scenarios, with implementations often including interactive parameter adjustment interfaces.