MATLAB Code Implementation for Predictive Function Control

In control system design, predictive function control represents a crucial control strategy primarily employed to optimize a system's response to future states. Implementing predictive function control through MATLAB enables convenient algorithm validation and parameter adjustment through simulation-based testing. The core concept of predictive control involves utilizing system models to forecast outputs over a future time horizon, then optimizing control inputs based on these predictions. This control approach proves particularly effective for systems with time delays or disturbances, as it can proactively compensate for potential impacts through anticipatory control actions. For step input scenarios, predictive function control ensures rapid and stable attainment of target values while minimizing overshoot. When external disturbances are present, predictive control adjusts control signals in advance according to model predictions, effectively suppressing fluctuations caused by disturbances through feedforward compensation techniques. Users can optimize control performance by adjusting parameters such as prediction horizon and control horizon. Longer prediction horizons generally enhance system stability but may increase computational load, while the choice of control horizon directly influences the system's dynamic response speed through input constraint handling. In practical applications, predictive function control has demonstrated excellent performance across various domains including chemical processes, robotics, and power systems. Within MATLAB's simulation environment, engineers can easily validate control performance under different parameter configurations using built-in optimization tools and control system toolbox functions, facilitating identification of optimal settings through systematic parameter sweeps and performance metric evaluations.