Predictive Control MATLAB Implementation Using Dynamic Matrix Control Method

This implementation focuses on the Dynamic Matrix Control (DMC) method for predictive control, which can be effectively applied in automation control systems. The DMC approach is a state-space model-based predictive method that forecasts future system states and outputs. The method operates by continuously updating the state-space model online and performing predictions based on the latest measurement values and control inputs. In MATLAB implementation, this typically involves constructing a dynamic matrix from the system's step response coefficients and solving a quadratic optimization problem to determine optimal control moves. Key algorithmic components include: calculating the prediction horizon using system response data, implementing constraint handling through quadratic programming, and incorporating feedback correction mechanisms to compensate for model uncertainties. The code structure generally consists of three main modules: model identification for obtaining system parameters, prediction generator for state forecasting, and optimizer for computing control actions. This approach enables superior control performance in real-time applications by anticipating system behavior and optimizing control sequences accordingly. Furthermore, the method supports multivariate control systems, allowing simultaneous prediction of multiple variables' future states and outputs through matrix operations and multi-input multi-output (MIMO) formulation. The implementation handles cross-coupling effects between variables using partitioned matrix structures. Therefore, Dynamic Matrix Control represents a highly effective control methodology that plays a significant role in automation control systems, particularly in industrial processes requiring precise multi-variable regulation.