Dynamic Matrix Control Algorithm Implementation and Simulation in MATLAB for Predictive Control

This article provides an in-depth exploration of predictive control algorithms, with specific focus on the Dynamic Matrix Control (DMC) algorithm and its implementation through MATLAB programming and simulation. We begin by introducing the fundamental concepts and objectives of predictive control algorithms, explaining the critical role DMC plays in industrial process control applications. The discussion then progresses to detailed explanations of DMC's underlying principles, including step response modeling, prediction horizon calculation, and control law formulation using quadratic programming optimization.

We further elaborate on practical implementation methodologies, providing code examples that demonstrate key MATLAB functions such as dlqr for control gain calculation, conv for convolution operations in prediction vector generation, and quadprog for solving the optimization problem. The article includes comprehensive MATLAB simulation procedures covering steps from system identification using impulse response data to closed-loop performance evaluation through sim commands.

Through case studies involving typical industrial processes like temperature control systems and level regulation applications, we illustrate complete DMC implementation workflows. These examples showcase practical coding techniques for handling constraints, tuning prediction and control horizons, and analyzing system robustness. Finally, we critically evaluate DMC's advantages in handling process constraints and its limitations regarding computational complexity, while discussing potential enhancements such as adaptive DMC formulations and real-time implementation considerations using MATLAB's Real-Time Workshop.

By studying this material, readers will gain comprehensive understanding of predictive control fundamentals, master DMC algorithm implementation techniques, and develop practical MATLAB programming skills for control system simulation and analysis.