Generalized Predictive Control Algorithm Simulation

This article explores the simulation of generalized Predictive Control (GPC) algorithm. The implementation employs Recursive Least Squares (RLS) method for online system identification and provides comparative analysis with traditional PID control algorithms, offering new perspectives for control system optimization. The simulation framework allows parameter adjustments to emulate various operational conditions, enabling comprehensive study of algorithm performance across different scenarios. Notably, the GPC algorithm's application extends beyond control systems to domains like finance and environmental engineering. Through in-depth investigation of this algorithm, practitioners can acquire advanced technical capabilities for solving complex real-world problems. The implementation typically involves key components such as: prediction horizon configuration, cost function minimization using quadratic programming, and real-time parameter adaptation through covariance matrix updates in the RLS routine.