Design of an intelligent controller for a grain dryer: A support vector machines for regression inverse model proportional-integral-derivative controller.

Aini Dai,Xiaoguang Zhou,Zidan Wu

doi:10.1002/fsn3.1340

Abstract

Grain drying control is a challenging task owing to the complex heat and mass exchange process. To precisely control the outlet grain moisture content (MC) of a continuous mixed‐flow grain dryer, in this paper, we proposed a genetically optimized inverse model proportional–integral–derivative (PID) controller based on support vector machines for regression algorithm which is named the GO‐SVR‐IMCPID controller. The structure of the GO‐SVR‐IMCPID controller consists of a genetic optimization algorithm, an indirect inverse model predictive controller, and a PID controller. In addition, to verify the control performances of the proposed controller in the simulation study, we have established a nonlinear mathematical model for the mixed‐flow grain dryer to represent the nonlinear grain drying process. Finally, the control performance and the robustness of the GO‐SVR‐IMCPID controller were simulated and compared with the other controllers. By the simulation results, it is shown that this proposed algorithm can track the target value precisely and has fewer steady errors and strong ability of anti‐interference. Furthermore, it has further confirmed the superiority of the proposed grain drying controller by comparing it with the other controllers.

Highlights

Studying the grain drying control is significant (Dai, Zhou, & Zhou, 2017; Dai, Zhou, Liu, Liu, & Zhang, 2017, 2018; Liu & Arkema, 2001; Liu & Bakker-Arkema, 2001; Mujumdar, 1995).Grain drying control is a challenging task owing to the complex heat and mass exchange process
To further study the support vector machines for regression (SVR) controller for the grain dryer, in this paper, we proposed a SVR indirect inverse model controller and compared it with different controllers including the previously designed GO-SVR-IMPC controller
The proposed controller is called for short GO-SVR-IMCPID in this paper, of which the structure is shown in Figure 8, where u1 is the output of the SVR inverse model gsvr, uPID is the output of the PID controller, u is the superposition of u1 and uPID, and e is the error between y and r

Summary

Introduction

Studying the grain drying control is significant (Dai, Zhou, & Zhou, 2017; Dai, Zhou, Liu, Liu, & Zhang, 2017, 2018; Liu & Arkema, 2001; Liu & Bakker-Arkema, 2001; Mujumdar, 1995).Grain drying control is a challenging task owing to the complex heat and mass exchange process. It is such a long-delay nonlinear process subjected to various affection factors that an accurate mathematical model is difficult to make.

Methods

Results

Conclusion