Aiming at the problem that the model of steady state optimization cannot be formed for one-order integrating process in twolayer predictive control, the concept of “critical steady state” was proposed and then the “point” model and the establishment conditions of “critical steady state”, i.e. stability condition, were presented. Thus the mathematic optimization model was built based on the steady property of one-order integral process. Two-layer predictive control is divided into upper steady state optimization layer and lower dynamic control layer. The optimal steady output target is obtained by the solution of the model of steady state optimization in the upper layer and also tracked by the model based control in the lower dynamic control layer. The solution process for the optimization problem is divided into two stages: feasibility stage and optimization stage. In the feasibility stage, the feasibility judgment and soft constraint adjustment method are presented for the stability condition of integrating process to guarantee the existence of the solution in the optimization stage. In simulation, the presented integral type twolayer predictive control algorithm is implemented on a multivariable integrating process. The optimal steady state output target can be calculated and also quickly tracked by this optimal control system.
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