Computable Optimization Control Method: Model Predictive Control Algorithm

This paper introduces an efficient optimization control method: the Model Predictive Control (MPC) algorithm. MPC operates as an online computational control strategy that performs dynamic optimization and regulation of systems. The algorithm employs a prediction horizon where future system behavior is forecasted using a mathematical model. At each sampling instant, it computes optimal control actions by solving a constrained optimization problem, typically implemented through quadratic programming or numerical solvers. Key implementation components include the prediction model, objective function formulation with weighting matrices, and constraint handling mechanisms. MPC has found extensive applications across industrial domains including chemical processing, mechanical systems, and power engineering. The algorithm demonstrates strong practicality and effectiveness as an efficient control methodology, with common implementations involving receding horizon control and real-time optimization updates.