Genetic Algorithm-Based Two-Layer Predictive Optimization Control

In this paper, a two-layer predictive optimization control strategy is proposed to address the complex optimization control problem for industrial processes. At the upper layer, a rigorous optimization issue is addressed with an economic objective function by utilizing the genetic algorithm (GA). The optimal setpoints are obtained online for different production scenarios and are then transmitted to the lower layer. At the lower layer, a model predictive control (MPC) method is employed to accurately track the decomposed optimal setpoints. Moreover, both feasibility of the optimization and closed-loop stability can be proved theoretically. Simulation examples verify the efficacy of the proposed strategy.