Model Predictive Control Using Dynamic Matrix Control Algorithm

Model Predictive Control (MPC) based on the Dynamic Matrix Control (DMC) algorithm represents a widely-used control strategy that optimizes future state variables and control inputs into a sequence of control actions to achieve system regulation. As a model-based control approach, MPC demonstrates strong adaptability and robustness characteristics. The algorithm implementation typically involves solving a quadratic programming problem at each sampling instant, where the cost function minimizes tracking errors while satisfying operational constraints. This method proves particularly effective for multivariate systems and systems with constraints, making it suitable for numerous industrial domains including chemical processing, mechanical systems, power systems, and environmental control applications. Code implementation often requires constructing a dynamic matrix from step response coefficients, formulating prediction equations, and implementing receding horizon optimization with constraint handling capabilities.