1. Introduction
Traditional control systems for brick tunnel kilns (BTK) often struggle to adapt the dynamic and nonlinear nature of the firing process. Many external factors such as weather conditions, variations in raw materials, and the demand for various models in the production process can reduce the efficiency and quality of brick products [
1,
2]. To address these challenges and improve brick production efficiency, brick factories have transitioned from manual to automatic production. The automatic system can precisely adjust the kiln’s heat according to the specific requirements at each stage of brick production. The quality of bricks produced by this automatic system is significantly better than that by manual control, and damage to bricks during firing is avoided because the temperature is automatically adjusted [
3,
4,
5]. This research focuses on developing an adaptive control system approach, specifically an adaptive fuzzy-neural control that conforms to the specific requirements, such as temperature control accuracy, energy savings, and environmental protection, of brick tunnel kilns.
In the BTK system, the identification aspect is a crucial part of the control design procedure. Due to the nonlinear characteristics of the BTK system, designing the controller using conventional approaches is ineffective. In [
6], the BTK system model was developed in two types, differing in the treatment of unsteady conditions with recorded kiln temperature data. While this model allowed for testing of physical principles, it has a drawback in designing the control system for the BTK. In [
7], Mancunhan et al. introduced a model of drying bricks in the preheating zone of the BTK system. However, the presented model was developed through a simulation study and lacks experimental work. The authors in [
8,
9,
10] provided a numerical simulation to evaluate the BTK thermal performance with several control methods. Nevertheless, the knowledge of the dynamic model of the BTK system is negligible. Therefore, formulating an accurate BTK system model is crucial to developing the control design system and enhancing brick production efficiency.
To prevent brick fires in automatic systems, researchers have devised various methods, including traditional proportional-integral-derivative (PID), robust control, adaptive control, intelligent control, and so on [
11]. The authors in [
11,
12,
13,
14] proposed a PID controller to adjust the thermal therm of the brick tunnel kilns. However, the tracking performance was poor with the low accuracy of temperature control due to the fixed control parameters. Refs. [
15,
16] presented a fuzzy logic controller (FLC) for a brick automatic temperature control system. Nevertheless, the design of the FLC is simplistic, featuring only one input and one output, which cannot guarantee good tracking performance. In pursuit of enhanced product quality and system reliability, scholars in [
17,
18,
19] proposed an adaptive fuzzy-PID controller to improve the tracking qualification. In addition, the authors in [
20,
21,
22] presented intelligent control for the BTK system. Each controller has distinct advantages but is still suboptimal in the temperature control process. Furthermore, there is a scarcity of experimental studies; most previous works focus on simulation studies for temperature control. Hence, the development of an advanced control algorithm capable of mitigating negative factors, along with providing experimental studies for brick tunnel kilns, is imperative.
Based on the above literature review, the FLS is frequently chosen as an effective tool for system regulation addressed to various negative factors; for instance, an unmodeled dynamics system. Nevertheless, the FLS is designed depending on the experience of the engineer, which is not capable of self-learning proficiency [
23,
24,
25,
26]. In recent decades, neural networks (NN) have been extensively researched in control algorithms, possessing energetic self-learning capabilities. It is noteworthy that both FLS and NN have their own merits and demerits in terms of control algorithms. To leverage the advantages of fuzzy logic techniques and neural networks [
27,
28], a new approach—fuzzy neural networks—has been investigated, possessing a structure well-suited for fuzzy reasoning and self-learning abilities. However, there are limited studies utilizing the fuzzy neural network algorithm for temperature control in the BTK system.
Motivated by the aforementioned analysis, this article aims to present the design, implementation, and evaluation of an adaptive fuzzy-neural controller to regulate the temperature in a tunnel brick kiln with high efficiency. The main contributions of this paper are summarized as follows:
- (1)
As far as the authors are aware, this is the first instance of a fuzzy neural network controller proposed for the BTK system, combining the capabilities of fuzzy logic and neural networks. The advanced control system aims to offer a robust and adaptable solution to optimize temperature levels while minimizing energy consumption for the BTK system.
- (2)
The controller leverages sensor data, historical information, and real-time adjustments to optimize temperature control, taking into account variables such as fuel type, external environmental conditions, and furnace load.
- (3)
Outstanding performance of the suggested methodology is exhibited via simulation and experimental results as compared with two controllers, i.e., PID and fuzzy controllers.
The structure of the article includes the following:
Section 2 includes system modeling and problem statements.
Section 3 includes control system design.
Section 4 includes implementation and evaluation results through simulation and experiment. Ultimately, the conclusion is summarized in
Section 5.
2. Modelling of the Brick Tunnel Kilns
The brick tunnel kilns (BTK) are nonlinear plants and very complex. In the BTK operation, the air and bricks go in opposite directions, as depicted in
Figure 1. Commonly, the BTK system includes three zones: preheating, firing, and cooling. Firstly, the bricks are dried to obtain a 12% moisture and then moved to the preheating zone. Herein, bricks are heated with the temperature about 700 °C. Next, the bricks enter the firing zone, and the temperature rises slowly to about 1000 °C. Finally, the bricks are cooled in the cooling zone with the desired temperature of approximate 30−50 °C.
In this paper, the simplified BTK system focuses on the temperature control of the firing zone, which is a significant stage during the brick production process. The model of the firing zone can be found in the previous work [
29,
30]. In detail, the testbench of the firing zone for the BTK system contains a solid-state relay, an arduino board, light bulbs, laptop-integrated Matlab/Simulink software 2020a, and the temperature sensor, as illustrated in
Figure 2. To facilitate the control design, the linearization step is applied to be able to find the simple brick tunnel kiln (BTK) model in particular. Herein, the System Identification tool in Matlab is utilized to formulate the transfer function of the presented system while using frequency-domain experimental data.
Firstly, the input and output data of the BTK system are recorded in the real system. Herein, the applied voltage input is a sinusoidal wave formed through Matlab to provide the BTK system and then get the returned data as output temperature, as depicted in
Figure 3. The input and output data are collected by two ‘To workspace’ blocks, namely, out.controlsignal and out.temperature, respectively. The data is written in the MATLAB base workspace.
After obtaining the input and output data of the system, we can find the transfer function of the system in the System Identification tool. This process includes four stages: Import data, Working data, Estimate (Transfer function model), and Import model.
Finally, the brick tunnel kilns can be described in the form of the transfer function with an accuracy of 88.52% via the presented tool.
Remark 1. The ideal of the control goal is to ensure an excellent temperature tracking performance, and enhance energy saving and environmental protection under external disturbance. Inspired by the merits of the fuzzy-neural network, the adaptive intelligent controller is constructed to obtain the control goal for the brick tunnel kiln.
3. Controller Design
This section provides the fuzzy-neural controller and two other controllers (PID and fuzzy controller) with the compared proposal. All controllers are presented as follows.
3.1. PID Controller
The Proportional-Integral-Derivative (PID) controller is a widely used feedback control system that operates based on three main components as shown in the below equation. These three components work together to maintain and adjust the system’s output in response to changing conditions.
where
kp,
ki, and
kd represent the P-gain, I-gain, and D-gain, respectively.
3.2. Fuzzy Logic Controller
The fuzzy controller is a control system based on the principles of Fuzzy Logic. Fuzzy Logic allows handling uncertain information and fuzzy sets to make decisions. Here, we design a fuzzy controller to control the temperature in the brick tunnel kilns. The fuzzy logic controller includes four stages: fuzzification, rule definition, fuzzy inference system, and defuzzification. For the BTK system, the error of the output temperature and its desired signal (
e) is taken as two inputs of the FLC to conduct one control signal (
u). Five triangle membership functions (MFs) are utilized to describe the input variable
e, such as VL (very low), L (Low), AV (Average), H (High), and VH (Very high) within the range of (0,20). And five gauss MFs are utilized to describe the input variable
de, such as NB (negative big), NS (negative small), ZE (zero), PS (positive small), and PB (positive big) within the range of (−1, 1). The output
u is characterized by five triangle MFs: VL (very low), L (Low), AV (Average), H (High), and VH (Very high) within the range of (0,5). The input of the fuzzy controller is the error between the set value and the output temperature of the BTK system, as depicted in
Figure 4. Meanwhile, the inhomogeneous MFs of the output are shown in
Figure 5. The rule base of temperature FLC for the BTK system is displayed in
Table 1.
The system output is the control signal of the BTK system after defuzzification with any input. Herein, the defuzzification of the FLC is designed to follow the centroid method as follows:
where
S is the determined set and
μ(
yi) is the membership value for point
yi in the universe of discourse.
3.3. Fuzzy-Neural Controller
For fuzzy logic, the engineer can easily develop a desired system using only If-Then rules, which are close to human processing. With the majority of applications, this allows for a simpler solution within a shorter time. However, along with the advantages of fuzzy control systems, there are still some experience requirements of designing and optimizing fuzzy logic systems in object control.
As for neural networks, they have some advantages such as parallel processing and fast processing speed. Furthermore, neural networks have the ability to learn and train the network to approximate any nonlinear function, especially when a set of in/out data is known. Nevertheless, the main drawback of neural networks is the difficulty to explain clearly how neural networks work. So, the regulation of neural networks is very difficult.
From
Table 2, it can be observed that if fuzzy logic and neural networks are combined, there will be a hybrid system with the advantages of both: fuzzy logic allows easy system design easily and explicitly, while neural networks allow learning what we require about the controller. It modifies the shape, position, and coherence dependency functions and automatically fits them completely. The structure of a three- layer fuzzy neural network with inputs
X(
t) = [
x1(
t),
x2(
t),
x3(
t)]
T and one output
u is depicted in
Figure 6. The ReLU is defined as follows:
f(
y) = max(0,
y). The input data source
X(
t) can be achieved from a sample dataset in array format.
The three-layered fuzzy neural network is formulated with M fuzzy If-Then rules as follows:
Rule Rl: If x1(t) is , x2(t) is , and xn(t) is , then the output u (control signal) is Bi,
where , and Bi denote the fuzzy sets.
The output of fuzzy neural can be obtained by
where
M defines the number of If-then rules,
is the adjustable weight, and
represents the MFs value of the fuzzy variable
Zk.
In this paper, for the sake of the fuzzy controller design, the Anfis tool is built in Matlab so we can train the fuzzy-neural network. In the command prompt, type “anfisedit” to enter the Anfis tool. Herein, the collected data is used to train the fuzzy-neural network. In addition, we can set up the network configuration as required.
The block diagram for the learning process of an adaptive fuzzy neural network is summarized in
Figure 7. Because of the supevisor learning approach, the training document is recorded that comprises three inputs and one output. Three inputs include tracking error, the integral of the error, and the derivative of the error, while the control effort is one output. The data is recorded to the simout workspace block in Matlab/SIMULINK under the temperature control of the FLC or PID controller. Since the collection of the sample data is finished, the general fuzzy inference system (FIS) is created. Herein, the number and the kind of membership function are selected. Then, the configuration for FIS is established, such as error tolerance, training method, and the number of epochs. The accepted output of the fuzzy-neural controller can be achieved since the training error is low or at the end of epochs.