Generalized Predictive Control (GPC)

Generalized Predictive Control (GPC) code represents a predictive control methodology that utilizes known system input and output data to forecast future system behavior, thereby generating control signals to achieve desired performance objectives. The GPC algorithm distinguishes itself through high-precision tracking capabilities and strong robustness, making it particularly suitable for applications in chemical process control, aircraft systems, robotics, and other industrial domains. The core implementation involves calculating optimal control sequences by minimizing a multi-step cost function that balances tracking errors and control efforts. Key algorithmic components include recursive parameter estimation using methods like recursive least squares, prediction horizon optimization, and constraint handling through quadratic programming solvers. The accuracy of the prediction model is critical in GPC implementations, prompting ongoing research into performance enhancements such as incorporating nonlinear elements through neural network approximations, adaptive controller parameter tuning via gradient-based optimization, and real-time constraint management techniques.