Intelligent control of a grain drying system using a GA-SVM-IMPC controller

The grain drying is difficult to control because of its characteristics of long delay, strong nonlinearity, and uncertainty parameters. The aim of the paper is to design a suitable controller for a newly designed grain dryer. First, a nonlinear math model of the wheat mixed flow drying was established and analyzed based on the fundamental laws of simultaneous heat and mass transfer. The simulations for the batch circulating drying process and the continuous grain drying process have been made and the simulation results show that it fits well with the actual drying process. Second, an internal model proportional integral derivative (PID) controller (IMPC) based on the support vector machine (SVM) algorithm and the genetic algorithm (GA-SVM-IMPC) was proposed from the view of the energy loss and the dried grain quality. The structure of the GA-SVM-IMPC controller consists of a SVM prediction model, a SVM inverse model controller, a PID controller and a genetic optimization algorithm. Finally, the effectiveness of this controller was demonstrated by computer simulations, and the comparative study with the other controllers further confirmed the superiority of the proposed dryer controller.