Predictive function control of output probability density function

Randomness is common in many real industrial processes,its modeling problem has always been an important research content in the field of industrial control.Probability density function(PDF) is an effective means to solve the problem of stochastic distributed system modeling. This paper based on B-spline basis function sets up the model of output and input data.Due to the fact that there are often many constraints in the actual industrial process and high requirements for real-time and accuracy,this paper proposes to combine the PDF model with predictive control to achieve effective tracking of system output setpoints.Predictive control optimization is more complicated and computationally intensive,so the predictive functional control (PFC) strategy is adopted to reduce the degree of freedom of the optimization problem.Applying PFC based on PDF modeling to the molecular weight distribution (MWD) of the polymerization process,and compared with generalized predictive control (GPC) algorithm,the validity of the proposed method is verified.