GPC Generalized Predictive Control Program Source Code

The source code for the GPC generalized predictive control program implemented as a console application is provided below. The GPC control algorithm is an advanced model-based predictive control technique that utilizes a CARIMA (Controlled Auto-Regressive Integrated Moving Average) model to predict future system behavior. The implementation typically involves key mathematical operations such as Diophantine equation solutions for prediction computations, cost function minimization using recursive methods, and receding horizon control strategy. The algorithm calculates optimal control sequences by minimizing a quadratic cost function that weights both tracking errors and control efforts over a prediction horizon. This console-based implementation allows for straightforward execution on local machines and provides a clear structure for understanding the core GPC computational flow. The code architecture typically includes modules for system identification, prediction calculation, optimization solver, and control law implementation. Users can modify parameters such as prediction horizon, control horizon, and weighting factors to adapt the controller to specific dynamic system requirements. The source code demonstrates practical implementation details including handling of system constraints, real-time data processing, and numerical stability considerations in the optimization process. Below you will find the complete source code for the GPC generalized predictive control program implemented as a console application, featuring well-commented sections for easy customization and integration with various control systems.