The Application of MPC Grey Theory Algorithm in the Design of Synchronous Predictive Controller
With the development and optimization of AI algorithms and prediction models,for complex cross-cou-pled environments and objects,where it is difficult to establish mathematical models or the accuracy of test meth-ods is not high,it is difficult to ensure effective control scheme operation and algorithm application.Firstly,based on the analysis of the interference model and the prediction principle of synchronous control,empirical val-ue theory with MPC(Model Predictive Control)grey theory algorithm is introduced and the principle of grey pre-diction algorithm is the optimal solution of the mean square value of the constraint set.Secondly,the variable in-put parameters are integrated into a mathematical model for model matching of several segments and the time axis of its control system can establish a complete time model with discrete time series.It is beneficial to analyze and solve complex models or systems that cannot be described by traditional numbers,especially the incomplete or distorted parameter information.Finally,it is analyzed that industrial control models are mostly time-delay con-trol objects with pure lag second-order inertia links.Through three sets of modeling and simulation experiments,it is concluded that the design of synchronous predictive controller based on MPC grey theory algorithm has better advantages,which belongs to a multivalued hybrid dynamic algorithm and has a good synchronous effect in error adjustment and fluctuation suppression.
error compensationfluctuation suppressionsynchronous controllerpredictive controlMPC grey theory algorithm
万方数据
维普
