Model Predictive Control Program with Variable Prediction Horizon Simulation

This model predictive control program implements a robust simulation framework capable of testing various prediction horizons (P) through configurable parameter settings. The core algorithm employs a receding horizon control strategy where at each time step, an optimization problem is solved over the prediction horizon while applying only the first control input. The program's architecture includes key functions for system modeling, constraint handling, and quadratic optimization solving, typically implemented using MATLAB's quadprog function or similar optimization tools. The simulation results demonstrate excellent curve characteristics, showing smooth control responses and effective constraint management. By systematically varying the prediction horizon parameter P, users can perform comparative analysis of control performance under different predictive scenarios. This capability allows for thorough investigation of system behavior across varying conditions, making it particularly valuable for control strategy design and system optimization. The code structure features modular design with separate functions for: 1) State prediction using discrete-time system models 2) Cost function formulation with weighting matrices 3) Constraint implementation (input/output limits) 4) Real-time optimization solving This design enables users to easily modify system parameters, adjust constraints, and analyze how prediction horizon selection impacts control performance, ultimately facilitating the development of optimal control strategies for the target system.