Generalized Predictive Control Simulation

The Generalized Predictive Control (GPC) simulation program serves as an essential tool in control systems engineering, utilizing recursive algorithms to predict output values at future time instances based on historical data patterns. Through the implementation of cost function optimization and receding horizon control strategies, the program achieves accurate trajectory forecasting, allowing for better understanding of system dynamics and informed decision-making. The core algorithm typically involves solving Diophantine equations and employing minimization techniques to compute optimal control sequences. Furthermore, the program incorporates reference tracking capabilities through weighted objective functions, enabling real-time performance evaluation against desired trajectories. These computational features make GPC simulation programs widely applicable across industrial process control, automotive systems, and robotics domains, with implementations often leveraging MATLAB's Control System Toolbox or Python's predictive control libraries for efficient matrix operations and constraint handling.