Drying is an energy intensive and complex nonlinear process and it is difficult to control and make the traditional control meet the challenges. In order to effectively control the output grain moisture content of a combined infrared radiation and convection (IRC) grain dryer, taking into account the superiority of the fuzzy control method in dealing with complex systems, in this article, a genetic optimization dual fuzzy immune PID (ProportionalIntegralDerivative) (GODFIP) controller was proposed from the aspects of energy savings, stability, accuracy, and rapidity. The structure of the GODFIP controller consists of two fuzzy controllers, a PID controller, an immune algorithm, and a genetic optimization algorithm. In addition, a NARX model which can give relatively good predictive output information of the IRC dryer was established and used to represent the actual drying process to verify the control performance in the control simulation and antiinterference tests. The effectiveness of this controller was demonstrated by computer simulations, and the antiinterference performance comparative study with the other controllers further confirmed the superiority of the proposed grain drying controller which has the least value of performance objective function, the shortest rising time, and the best antiinterference ability compared to the other three compared controllers.
Grain drying control is very important because it can decrease the grain loss by controlling the grain moisture and temperature to the desired level and maintain the quality, freshness, and longer life storage of grain. There are parameter uncertainties or variations in the grain dying process, and the establishment of a good control system for a grain dryer is difficult. Drying is an energy intensive process, one of the main control objectives is to dry the moisture content to desired level with efficient energy consumption and good quality, besides that stability of the system and robustness of the controller towards any disturbances are also the fundamental requirements of a dryer controller [
In this research, the control object is a combined infrared radiation and convection (IRC) grain dryer designed by our research group which combines the direct and indirect heating drying technology. Infrared radiation drying is a new drying technology developed in recent 30 years and now has become more popular because of its advantages, such as its low drying time, the reasonable quality of the final dried product, and its greater energy savings capability, in addition to its lower price compared to microwave and vacuum drying methods [
Traditional control methods have encountered a lot of obstacles because grain drying is a complicated heat and mass transfer process which is characterized by long delay process, highly nonlinearity, multidisturbance, strong coupling, and so on. It is difficult to make an accurate mathematical model of grain drying, so the control of grain drying is a challenging job [
In all, a drawback of the abovementioned studies is that the authors generally made several simplifications in developing the dryer mathematical model based on some assumptions and observations. These simplifications are expected to affect the performance of these models and consequently their reliability in representing the real process when using these models for control purposes. Moreover, the mathematical models generally consist of sets of highly complex and nonlinear partial differential equations (PDES) with several auxiliary algebraic equations that involve transfer coefficients and thermophysical properties that require highly complicated numerical techniques to solve, rendering them undesirable options in control systems [
Since the 1970s, the research development of computer control technology and artificial intelligence has provided new ways for advanced control of the grain drying; thereafter drying control comes into the intelligent control period, of which the fuzzy logic controller (FLC) is a typical intelligent controller which imitates humans’ decisionmaking and common sense [
Fuzzy immune PID controller based on the immune feedback mechanism combines the intelligent FLC with the traditional PID controller and has the advantages of simple, good robustness and independence on the system model, which uses the characteristics of fuzzy control to learn the biological immune feedback mechanism under the complex disturbance and uncertain environment [
In all, based on the idea of artificial intelligence, this paper proposes an improved fuzzy immune PID controller combined with two kinds of evolutionary algorithms: the immune feedback algorithm and the genetic optimization algorithm, which has improved the limitation of the traditional PID controller and the general fuzzy immune PID controller. Because the algorithm adopts two kinds of fuzzy controller and uses the genetic algorithm to optimize the initial controller parameters of the model, the proposed controller in this paper is called the genetic optimization dual fuzzy Immune PID (GODFIP) controller. Based on the GODFIP, the speed of discharging grain motor can be automatically adjusted to achieve the precise control of the output grain moisture of the IRC grain dryer according to the difference and its change rate between the output grain moisture content and the target moisture content. Finally, the NARX (Nonlinear Autoregressive models with Exogenous Inputs) model is used to represent the actual drying process to test the effectiveness of the proposed controller, and the comparative study with the other related controllers is also made, and the simulation results show that the control effect of GODFIP is better than that of other compared controllers.
The IRC grain drying system has been put into use in Harbin Development Zone, Binxi town, China, Dongyu Machinery Co. Ltd. Fresh, mature corns were purchased from a local farm (an agricultural area in north of China).
The IRC grain dryer mechanism system is shown in Figure
Mechanic structure diagram of the IRC grain dryer. (1) Bucket elevator T1, (2) wet grain barn, (3) belt conveyor P1, (4) bucket elevator T2, (5) belt conveyor P3, (6) belt conveyor P5, (7) dried grain barn, (8) bucket elevator T3, (9) belt conveyor P4, (10) dryer, and (11) belt conveyor P2.
Scheme of the IRC grain dryer. (1) Main hot air speed. (2) Hot air temperature. (3) Infrared exhaust gas temperature. (4) Infrared exhaust gas velocity. (5) Exhaust gas temperature and humidity. (6) Drying waste gas. (7) Inlet grain temperature and moisture. (8) Outlet grain temperature and moisture. (9) Postdrying grain temperature. (10) Infrared grain temperature. (11) Combustion tube temperature. (12) Fluegas temperature. (13) Ambient temperature and humidity.
The controlling situation and some equipment for the grain dryer are shown in Figure
Controlling situation and equipment for the IRC grain dryer.
The control system of the IRC dryer is also equipped with a computer, which is connected with PLC (S7300) through Ethernet. The experimental data can be analyzed and processed in the computer, and different drying algorithms can be designed and tested.
As seen from Figures
In the whole drying, the speed of discharging grain motor can be adjusted automatically by an intelligent control algorithm every time interval or adjusted manually by an experience worker according to the detected drying parameters.
For the complex IRC grain dryer, the description of the dynamic process of grain drying is more difficult. It is an effective way to learn the characteristics of the drying process by modeling the input and output data [
Autoregressive Exogenous (ARX) model has been widely applied in the prediction control. It does not need to know the physical mechanism inside the complex process, so it is regarded as a “black box” model. It provides a fast and efficient solution to the actual system output by means of a least squares approach, and it has the advantages of simple structure and strong robustness. It is an autoregressive model which has exogenous inputs, and it relates the current output value of a time series to past output values of the same series and current and past values of the driving (exogenous) series. More details about ARX can be found in [
The input and output data for identification model of grain drying are from the drying experiment of the IRC grain dryer (corn mixed flow and radiation) in December 4, 2015, a total of 384 sets of data, and the sampling frequency is 60 HZ. The input data of the identified drying model is the current and past drying time of grain being experienced in the dryer and the past output grain moisture content of the grain dryer; the output data of the model is the current output grain moisture of the grain dryer real detected by the output grain moisture sensor which has been calibrated using the 105°C standard oven method (GB 54971985).
The corn of the drying experiment purchased from the local farmers is a natural harvest species: number 1 XingXing (a breed name of corn), and its initial grain moisture content is about 26%. The ambient temperature is about minus 10°C, relative humidity 60–70%. The hot air temperature is between 80 and 120°C, and the hot wind speed is 12 m/s.
By using the identification toolbox in Matlab, ident, the identified transfer function of the model is a twoorder lag system as shown in (
In this study, we use mean squared error (MSE) and squared correlation coefficient
Experimental model identification results are shown in Figure
Fitting effect curve of the ARX model with the actual curve.
The theory of linear systems identification is a relatively matured field [
The NARX model has been proven to have a superior performance and has been successfully employed in solving various types of complex nonlinear model problems in recent years [
The architecture of the NARX model.
In the case of modeling, the inputoutput data of 384 samples are randomly divided into three parts to develop a NARX model: 268 training datasets, 58 validation datasets, and 58 testing datasets. During the training phase, the past input and output data of training are presented to train and adjust the neural network by using the LevenbergMarquardt algorithm; during validation, the validation data are used to measure network generalization and to halt training when generalization stops improving; during testing, the testing data have no effect on training data, so an independent measure of network performance can be provided during and after training.
Table
The simulation prediction results of the NARX model.
Data  Size  Model prediction results  

MSE 
 
Training data  268 

99.68% 
Validation data  58 

99.78% 
Testing data  58 

99.78% 
The prediction results of the NARX model on the testing data.
There are many factors that affect the control performance of grain drying as shown in Figure
Factors affecting grain drying.
Under a certain period of time and environmental conditions, some variables can be thought to be unchanged for a certain batch of grain drying, such as the grain initial temperature and initial moisture content, the ambient environment temperature and moisture, the hot air temperature, humidity, and the hot air flow rate. Usually, in the engineering practice of grain drying, the drying time is often taken as the control variable and the output grain moisture content as the important controlled variable; the other affection factors are taken as the disturbance signals.
The designed control scheme of this paper is shown in Figure
Block diagram of the control model scheme for the IRC grain dryer.
Biological immune system can produce antibodies against a foreign invasion of the antigen, which plays the defense role. The most important cells in the immune system are the lymphocytes which are mainly two kinds: B and T cells; B cells are responsible for antibody production and carry out the immunity function and T cells regulate the whole immune process. T cells are composed of inhibit T cells (
Assuming that the
Among them
Among them,
Imitating the above immune feedback mechanism, a
The discrete form of the ordinary PID controller is as shown in
The discrete output of the fuzzy immune PID is shown in (
Its controller structure is shown in Figure
Structure of the fuzzy immune PID feedback controller.
The Mamdani fuzzy controller designed in the control algorithm is used to approximate the nonlinear function (
Firstly, the fuzzy method of the input variables should be used to transform from the basic domain to the corresponding fuzzy set domain and define the quantification factor of input variables (
Secondly, the membership functions of input and output lingual variables should be formed to determine the distribution of different variables. There are commonly three types of membership function to be used: (1) normal distribution; (2) triangle; (3) trapezoidal. The numbers of the fuzzy sets for the input variable and output variable are used to meet the requirements of accuracy. As the numbers of the fuzzy sets increase, the numbers of fuzzy control rules increase accordingly, which will improve the accuracy of control, but meantime the complexity of control is increased; on the premise of satisfying the requirement of control precision, the least numbers of the fuzzy sets can be equal to 3 based on the principle of determining the minimum inference rules numbers; in addition, the numbers of the fuzzy sets for the input and the output can be unequal; in order to improve the accuracy of control, the fuzzy sets numbers for the output variable can be increased [
Knowledge rules of the fuzzy controller.




P  Z  N  

P  NB  NS  PS 
Z  NS  Z  PS  
N  NS  PS  PB 
Degree of membership function plots (a)
And then, the fuzzy output can be obtained by the fuzzy inference synthesis algorithm according to the rules of Table
Finally, according to the fuzzy rules, defuzzification will transform the output
It can also be seen from (
In addition, in the process of control, there is some difficulty in selecting the parameters (
Therefore, in this paper, a genetic optimization dual fuzzy immune PID (GODFIP) controller is designed, which not only can improve the limitation of the general fuzzy immune PID control but also can find the optimal parameters (
Structure of the genetic optimization dual fuzzy immune PID (GODFIP) controller.
In Figure
Taking the output grain moisture
In the drying experiment, by the experiment of calculating the grain weight of being discharged from the dryer within an hour (20 HZ: 3.126 t/h; 10 HZ: 1.753 t/h), the linear inverse relationship between the speed of discharging motor and the drying time is obtained
A brief introduction to the design principle of fuzzy PID parameter controller and genetic algorithm of the control structure of GODFIP are as follows (Sections
In Figure
PID parameters tuning rules of fuzzy PID parameters controller.







NB  NM  NS  ZE  PS  PM  PB  NB  NM  NS  ZE  PS  PM  PB  NB  NM  NS  ZE  PS  PM  PB  

NB  PB  PB  PM  PM  PS  ZE  ZE  NB  NB  NM  NM  NS  ZE  ZE  PS  NS  NB  NB  NB  NM  PS 
NM  PB  PB  PM  PS  PS  ZE  ZE  NB  NB  NM  NS  NS  ZE  ZE  PS  NS  NB  NB  NB  NM  PS  
NS  PM  PM  PM  PM  ZE  NS  NS  NB  NM  NS  NS  ZE  PS  PS  ZE  NS  NM  NM  NS  NS  ZE  
ZE  PM  PM  PS  ZE  NS  NM  NM  NM  NM  NS  ZE  PS  PM  PM  ZE  NS  NS  NS  NS  NS  ZE  
PS  PS  PS  ZE  NS  NS  NM  NM  NM  NS  ZE  PS  PS  PM  PB  ZE  ZE  ZE  ZE  ZE  ZE  ZE  
PM  PS  ZE  NS  NM  NM  NM  NB  ZE  ZE  PS  PS  PM  PB  PB  PB  NS  PS  PS  PS  PS  PB  
PB  ZE  ZE  NM  NM  NM  NB  NB  ZE  ZE  PS  PM  PM  PB  PB  PB  PM  PM  PM  PS  PS  PB 
Genetic algorithm is a stochastic global optimization method that mimics the metaphor of the natural biological evolution. According to the fitness function value, the global optimal solution can be obtained by the genetic evolution. The fitness value of each individual in the population is calculated by the fitness function and provided to the operator for selection, crossover, and mutation and screening individuals to find the best by retaining the best fitness value and eliminating the poor fitness values. If the termination condition is satisfied, then the optimal individual is used to be assigned to the parameters of the controller; otherwise continue to calculate the new species until the global optimal value is found.
So we can use genetic algorithm to optimize the control parameters (
Parameter coding: real code is adopted, and for a given parameter range [min, max], the real number coding is equal to
Population initialization: the individual coding length is
Determining fitness function: the minimum objective function of parameter selection is obtained from the aspects of reducing energy loss, stability, accuracy, and rapidity and to be provided for the operator selection and judgment. To prevent the control input of the controlled object too large and save the energy consumption, the output of the fuzzy immune controller is also added to the objective function, and the optimal function of the controller is shown in
In order to avoid overshoot, the penalty function is adopted as shown in (
In order to verify the effectiveness of the proposed GODFIP controller in the grain drying control, the following controllers, the general PID controller, the fuzzy PID controller, the fuzzy immune PID controller, and the GODFIP controller, are, respectively, designed and simulated to be compared with the GODFIP controller by programming in the Matlab. As can be seen from Section
During the simulation, the controlled variable is the output grain moisture content
The optimal solutions of control parameters of the GODFIP controller are evaluated by a realcoded genetic algorithm. The fitness curve of the optimization simulation is shown in Figure
The fitness curve of optimization simulation for the GODFIP controller.
(1)
In order to achieve a fair comparison, the genetic optimization algorithm is also used to optimize the other three controllers. Moreover, three runs of the genetic optimization algorithm program are made to avoid the stochastic error for each controller; finally, the average value of the objective function value
Figure
Control performance comparisons of different controllers.
Controllers 







General PID  26.046  0.13%  0.1502  57  0  39 
Fuzzy PID  20.348  0.07%  0.1501  20  0  30 
General fuzzy immune PID  1.7052  0.00%  0.1500  8  0  13 
GODFIP  1.6450  0.00%  0.1500  5  0  10 
Simulation results comparison of different controllers for the grain drying process of IRC dryer.
The rising time to the target value
Maximum overshoot
Adjusting time
The adjusting time
The dynamic influence factors of the grain drying process are a lot, and the influence of various disturbance factors in the control process easily leads to the variations of the output grain moisture. In order to verify the antiinterference performances of the controllers, the interference signal with the amplitude of 0.02 at sampling number 105 is added to represent a possible increase in the initial moisture of grain that enters the IRC dryer which is shown in Figure
The antiinterference effect comparisons between the GODFIP controller and the other controllers: (a) the interference signal and (b) the antiinterference effect comparison results.
Under the same optimization conditions, the PID controller and the fuzzy PID controller need more samples to the target value than the other two immune controllers (the GODFIP and the fuzzy immune PID) in order to accomplish the optimal control target; in fact, it is impractical for the grain drying process because it will cause an inefficient energy consumption and bad dried grain quality. The GODFIP controller and the fuzzy immune PID controller are both superior to the PID controller and the fuzzy PID controller, which not only can the stability of the control system be achieved, but also the system output can be rapidly adjusted to the target value, showing the advantage of the immune algorithm. In addition, the GODFIP controller has performed better compared to the fuzzy immune PID controller, so the GODFIP controller is more suitable for the IRC dryer than the other compared controllers.
In this paper, a genetic optimization dual fuzzy immune PID (GODFIP) controller based on the immune feedback mechanism is designed and simulated to control an IRC grain dryer represented by an identified Autoregressive with Exogenous input (NARX) model. The NARX model has a higher model approximation accuracy (MSE:
The authors declare that they have no conflicts of interest.
Aini Dai mainly engaged in the research of grain drying control and intelligent control. Xiaoguang Zhou mainly engaged in the research of control theory and its application in engineering. Xiangdong Liu mainly engaged in the research of drying technology and theory of agricultural products.
The authors of this paper would like to acknowledge the China National Common Weal Industrial Special Scientific Research Funds for Grain Industry (no. 201413006) and the 111 Project (B08004) for funding of this study, the cooperation of the research group, and the experimental help of the engineers in Dongyu Machinery Co. Ltd., China.