Fuzzy Control Model Research Articles

Advanced Study: Improving the Quality of Cooling Water Towers’ Conductivity Using a Fuzzy PID Control Model

Cooling water towers are commonly used in industrial and commercial applications. Industrial sites frequently have harsh environments, with certain characteristics such as poor air quality, close proximity to the ocean, large quantities of dust, or water supplies with a high mineral content. In such environments, the quality of electrical conductivity in the cooling water towers can be significantly negatively affected. Once minerals (e.g., calcium and magnesium) form in the water, conductivity becomes too high, and cooling water towers can become easily clogged in a short time; this leads to a situation in which the cooling water host cannot be cooled, causing it to crash. This is a serious situation because manufacturing processes are then completely shut down, and production yield is thus severely reduced. To solve these problems, in this study, we develop a practical designation for a photovoltaic industry company called Company-L. Three control methods are proposed: the motor control method, the PID control method, and the fuzzy PID control method. These approaches are proposed as solutions for successfully controlling the forced replenishment and drainage of cooling water towers and controlling the opening of proportional control valves for water release; this will further dilute the electrical conductivity and control it, bringing it to 300 µS/cm. In the experimental processes, we first used practical data from Company-L for our case study. Second, from the experimental results of the proposed model for the motor control method, we can see that if electrical conductivity is out of control and the conductivity value exceeds 1000 µS/cm, the communication software LINE v8.5.0 (accessible via smartphone) displays a notification that the water quality of the cooling water towers requires attention. Third, although the PID control method is shown to have errors within an acceptable range, the proportional (P) controller must be precisely controlled; this control method has not yet reached this precise control in the present study. Finally, the fuzzy PID control method was found to have the greatest effect, with the lowest level of errors and the most accurate control. In conclusion, the present study proposes solutions to reduce the risk of ice-water host machines crashing; the solutions use fuzzy logic and can be used to ensure the smooth operation of manufacturing processes in industries. Practically, this study contributes an applicable technical innovation: the use of the fuzzy PID control model to control cooling water towers in industrial applications. Concurrently, we present a three-tier monitoring checkpoint that contributes to the PID control method.

Comparative Study of Fuzzy Logic Operators

Fuzzy logic is currently very relevant because it offers a new way to approach tuning and decision-making problems. In this paper, we discuss a comparative study between Zadeh fuzzy operators and probabilistic operators, which are used during the activation outputs of the rules, to see which of them maximize or minimize the result of the defuzzification, in a fuzzy inference system used to create the fuzzy command control model, in artificial intelligence. The aim of this paper is to conduct a comparative study of rule output values by applying Zadeh's fuzzy operators and those of probability when activating rule outputs; and not to create other applications or to compare our solutions with previous results. We point out that we have carried out this comparative study of Zadeh fuzzy operators and probabilistic ones by exploiting several numerical examples such as in [1, 2, 3, 4, 5]. But to lighten the writing of this article, knowing that the steps of fuzzy inference are manually tedious, we consider the control data of a house fan, with two inputs (temperature and humidity) and one output (fan speed) processed by Baali Sabeur & Mahmoudi Messaoud in 2022. We have presented the cuts of the outputs from Zadeh's methods compared to those called probabilistic. After Fuzzification, activation of the Rules outputs, Aggregation of the outputs and defuzzification, we identified the fuzzy operators which maximize and those which minimize the net outputs, among the two families.

Digital development of manufacturing industry in Yangtze River Delta based on fuzzy control model

Digital development of manufacturing industry in Yangtze River Delta based on fuzzy control model

Research and Simulation Analysis of Fuzzy Intelligent Control System Algorithm for a Servo Precision Press

With the rapid development of the manufacturing industry toward intelligence and flexibility, traditional mechanical presses are unable to meet the increased stamping requirements due to the difficulties in achieving variable speed control and changing slide motion trajectories. Servo presses, driven directly by servo motors, can realize the flexible control of press movement and have become the trend in the industry for future development. The independent research and development of servo press control systems has become a popular topic and challenge in the domestic press industry. This paper proposes a new type of press transmission mechanism suitable for high-precision stamping, analyzes the working principle of its transmission mechanism, and provides a detailed analysis and discussion on the main research methods of fuzzy intelligent control for servo presses, including fuzzy model and basic control algorithms, fuzzy modeling and control of nonlinear systems, fuzzy predictive control, and stability analysis of fuzzy control systems. Based on the principle of the control scheme, a closed-loop control system block diagram and mathematical model for servo high-precision presses are established. Taking the physical prototype parameters of the STSZG1-250 as an example, the control system is dynamically simulated and verified using Simulink modules. Finally, their role and effects in intelligent control, and prospects for the development of fuzzy intelligent control, are discussed.

Trusted Computing and Privacy Protection of Computer Internet of Things Nodes Based on Deep Fuzzy Control of Dynamic Learning Rate

Under the background of mobile Internet of Things, this paper focuses on the solutions of trusted computing and privacy protection of Internet of Things nodes. Based on the research of existing mainstream service discovery protocols and the deep fuzzy control theory of dynamic learning rate, this paper proposes a trusted computing algorithm for Internet of Things nodes in mobile Internet of Things,which takes into account service quality and user preference.This paper combines the depth fuzzy control model of IoT nodes proposed before, uses a normal data set to train it, and then makes it generate and play the prediction residuals. It will be further used to build a privacy protection model and realize the anomaly detection of privacy protection data in the Internet of Things. This paper designs and implements a trusted computing system using the Internet of Things platform,which has been tested. Experimental results show that, compared with previous elastic matching algorithms, the precision and recall of the new algorithm proposed in this paper are improved.

Effectiveness of the Fuzzy Logic Control to Manage the Microclimate Inside a Smart Insulated Greenhouse

Agricultural greenhouses incorporate intricate systems to regulate the internal climate. Among the crucial climatic variables, indoor temperature and humidity take precedence in establishing an optimal environment for plant production and growth. The present research emphasizes the efficacy of employing intelligent control systems in the automation of the indoor climate for smart insulated greenhouses (SIGs), utilizing a fuzzy logic controller (FLC). This paper proposes the use of an FLC to reduce the energy consumption of a greenhouse. In the first step, a thermodynamic model is presented and experimentally validated based on thermal heat exchanges between the indoor and outdoor climatic variables. The outcomes show the effectiveness of the proposed model in controlling indoor air temperature and relative humidity with a low error percentage. Secondly, several fuzzy logic control models have been developed to regulate the indoor temperature and humidity for cold and hot periods. The results show the good performance of the proposed FLC model as highlighted by the statistical analysis. In fact, the root mean squared error (RMSE) is very small and equal to 0.69% for temperature and 0.23% for humidity, whereas the efficiency factor (EF) of the fuzzy logic control is equal to 99.35% for temperature control and 99.86% for humidity control.

APPLICATION OF FUZZY MODELS TO CONTROL THE STREAM OF MESSAGES IN DATA TRANSMISSION INFRASTRUCTURES

The report presents one of the problems that arises in distributed data transmission infrastructures - node overload in data transmission when there is unstable communication between nodes. As a solution, a mechanism for controlling the message flow intensity based on a fuzzy model is proposed. The developed fuzzy control model has demonstrated its effectiveness in the network for collecting and transmitting quotes of financial instruments from various trading platforms to the information-analytical system of a financial company. Message queues at the sources have significantly decreased, while minimizing losses of quotes on unstable communication channels. Additionally, the possibility of more flexible adjustment of fuzzy control by expanding the number of metrics of input parameters of the fuzzy model has been explored.

Control of mobil robots in fuzzy environment

In this work, the application of fuzzy hardware control to the dynamic movement of an autonomous two-wheeled mobile robot under uncertainty is considered the most relevant topic of the day. There are also adverse reactions. These led to an imprecise mathematical model for the two-wheeled robotic test rig. Therefore, the goal of developing a control system for a two-wheeled robot with a fuzzy logic control algorithm was set. The development of fuzzy logic control systems according to modern applications is very important to create intelligent robots that can be used in wider applications. In this work, a fuzzy control model for a non-linear two-wheeled robot system is designed, followed by a real-time implementation of the developed control in a robot device. Finally, the experimental results of the system were analyzed and discussed, and the closed-loop response of the fuzzy logic two-wheeled robot system with PID control was tested. The fuzzy control has a better response than the PID controller. A similar effect was observed for the normalized speed of the motors, as less energy was required to balance and stabilize the two-wheeled robot with fuzzy control. One of the main advantages of the method proposed in the article is to enable the autonomous two-wheeled mobile robot to operate without collision with obstacles and with high dynamic accuracy in a low-obstacle environment. As a result of the simulation, the effectiveness and reliability of the change avoidance behavior in uncertain environments and the speed control of the two-wheeled mobile robot platform of the proposed fuzzy control strategy are realized.

Weed infestation control using a p-fuzzy model

In the context of the use of mathematical models to describe the population dynamics, the present work aims to propose a model using Fuzzy Rule Based Systems (SBRF) to describe the control of weed population dynamics, from the point of theoretical view. The input variable is the weed population (discretized into 6 classes: low, medium low, medium, medium high, high, very high) and the output is the population variation rate (discretized into 4 classes: low-negative, low-positive, medium-positive, high-positive). In order to control the population a fuzzy control model is presented. The control model is coupled to the p-fuzzy population model. Numerical results illustrate the evolution of weeds population and we concluded that the p-fuzzy model proposed showed results satisfactory when compared to the weed dynamic model.

ПИТАННЯ ФОРМУВАННЯ МУЛЬТИ-НЕЧІТКИХ МОДЕЛЕЙ І ПРОЦЕДУР СИСТЕМ МОНІТОРИНГУ ПРОГРАМНОГО ЗАБЕЗПЕЧЕННЯ

The article details issues related to the features, tasks and the development of fuzzy models and intelligent procedures designed for software monitoring systems. At the same time, improved forms of fuzzy control models, called multi-fuzzy, are proposed. They generalize the methods of fuzzy modeling in the structure of rules and procedures by representing the dependence of the rules in the knowledge base, establishing various models of input data and applying various fuzzy inference methods for individual knowledge base rules.

Application of fuzzy prediction control model based on neural network in teaching resource recommendation and matching

As technology evolves, the allocation and use of educational resources becomes increasingly complex. Due to the many factors involved in recommending and matching English education resources, traditional predictive control models are no longer adequate. Therefore, fuzzy predictive control models based on neural networks have emerged. To increase the effectiveness and efficiency of using English educational resources (EER), this research aims to create a neural network-based fuzzy predictive control model (T-S-BPNN) for resource suggestion and matching. The results of the study show that the T-S-BPNN model α proposed in the study starts from 0 and increases sequentially by 0.1 up to 1, observing the change in MAE values. The experiment’s findings demonstrate that the value of MAE is lowest at values around 0.5. The T-S-BPNN model, on the other hand, gradually plateaued in its adaptation rate up to 7 runs, reaching about 9.8%. The accuracy rate peaked at 0.843 when the number of recommendations reached 7. The recall rate also peaked at 0.647 when the number of recommended English courses reached 7. The R-value for each set hovered around 0.97, which is a good fit. And the R-value of the training set is 0.97024, which can indicate that the T-S-BPNN model model proposed in the study fits well. It indicates that the algorithm proposed in the study is highly practical.

Modeling of nonlinear supply chain management with lead-times based on Takagi-Sugeno fuzzy control model

In this study, we present a novel fuzzy control strategy that considers the influence of lead times on nonlinear supply chain management (SCM) systems. The authors have constructed a fuzzy control model for an SCM model based on nonlinear phenomena with lead times, drawing inspiration from Takagi-Sugeno's (T-S) fuzzy control system. Additionally, the authors have designed a fuzzy H∞ control approach to mitigate the effects of lead times, switch between sub-models, and address customers' stochastic requests, all while considering the concept of maximal overlapped rules groups. The control of nonlinear supply chain management is implemented in a manner that ensures asymptotic stability and facilitates smooth transitions from one subsystem to another. The introduction of the membership function into the fuzzy system results in reduced fluctuations in the system's variables. The key contributions of the research include the design of a fuzzyH∞ control approach, leveraging maximal overlapping-rules group (MRRG) to ensure stability through localized definite positive matrices identification. This strategy achieves two primary objectives: asymptotic stability of the supply chain system and smooth switching between nonlinear SCM elements. The authors demonstrate the effectiveness of their proposed method through comprehensive comparisons with the fuzzyH∞ control technique. The study provides a valuable insight into managing lead times in SCM through dynamic and stable control methodologies. Finally, a comprehensive comparison is provided between the suggested H∞ management approach and the fuzzyH∞ control strategy. This comparison is demonstrated through two-stage nonlinear numerical simulations, keeping SCM lead times in mind. Movement results are presented to illustrate the effectiveness of our suggested algorithm.

Population dynamic study of interaction between two predators and one prey

In the present study, we develop a set of ordinary differential equations that simulate the interactions of an ecological system with two predators and one prey. Here, we have investigated the interaction dynamics between one prey and two predators. The three dimensional ‘Lotka-Volterra prey-predator’ system’s stability has been investigated applying ‘Takagi-Sugeno (T-S) impulse control model’ and the Fuzzy impulse control model. After the model is created, numerical simulations are used to determine the model's global stability and fuzzy solution. Graphical representations are provided together with suitable explanation to understand the workings of our proposed model.

Simulation system of intelligent photovoltaic grid-connected inverter considering fuzzy PI control

The grid connected inverter is the core component of the photovoltaic grid connected power generation system, which mainly converts the direct current of the photovoltaic matrix into alternating current that meets the grid connected requirements, playing a key role in the efficient and stable operation of the photovoltaic grid connected power generation system.This paper uses fuzzy PI control model which to improve the performance of intelligent photovoltaic grid-connected inverter to simulate the intelligent photovoltaic inverter system, using mathematical analysis and reasoning methods for model analysis,adopts two-stage three-phase LCL grid-connected inverter, establishes mathematical models in two-phase synchronous rotating and two-phase static coordinate systems, and adopts an active damping strategy based on grid-connected current. Based on existing research and empirical analysis,aiming at the disadvantage of poor dynamic response of repetitive control, an improved repetitive control strategy is adopted, and the controller is analyzed from two aspects of stability and dynamic performance, and the simulation model of photovoltaic grid-connected power generation system is built. Use experimental analysis method to verify the effectiveness of the model in this article,The experimental results show that the simulation system of intelligent photovoltaic grid-connected inverter considering fuzzy PI control proposed in this paper has certain effects.

Application of the Fuzzy Mamdani Method for Predicting Rainfall in the City of Yogyakarta

Weather and climate play an important role in everyday life, influencing various aspects of society and the environment. The city of Yogyakarta as a cultural center cannot be separated from the impacts of climate change, including rainfall patterns which can have significant effectiveness on daily activities, agriculture and infrastructure. This research uses a fuzzy logic methodology approach with the Mamdani method to predict rainfall. Fuzzy logic has proven to be effective in cold situations and is able to overcome the challenges of missing or incomplete data. The data used in this research was obtained from Bappeda DIY. The implementation for predicting rainfall levels uses Matlab by utilizing the provided Fuzzy Logic Matlab Toolbox. Based on research conducted using a fuzzy logic test to calculate all research accuracy data, an average error value in RMSE was obtained of 39.88%. These results indicate that there is significant error in these predictions, indicating that the fuzzy logic control model is not completely accurate. Therefore, it is necessary to review the data used in this prediction to ensure its accuracy.

Fuzzy MIMO model for efficient control of complex processes with uncertainties and nonlinearities

This paper shows the impact of the MIMO model on control system solutions for efficient control of complex nonlinear processes (multiple input, multiple output model) in terms of efficiency, product quality and energy efficiency. In modern automatic control systems, there is a need for increased capacity in long-distance data transmission, high-speed local area networks, etc. Capacity can be increased by extending the frequency range. However, the application of these techniques is limited by biosafety requirements, limited power supply (in mobile devices), and electromagnetic compatibility. Therefore, if these approaches to communication systems do not ensure the speed of transmission of information about technological processes, a weak correlation will appear. The values of indicators for the construction and use of fuzzy MIMO models of control decisions are expressed in the form of linguistic variables. The fuzzy MIMO model makes qualitative decisions in the control of complex processes. This includes uncertainty and nonlinearity problems, which are used to optimize complex systems.

Implementation of a Combined Fuzzy Controller Model to Enhance Risk Assessment in Oil and Gas Construction Projects

Implementation of a Combined Fuzzy Controller Model to Enhance Risk Assessment in Oil and Gas Construction Projects

An electronic medical record access control model based on intuitionistic fuzzy trust

Electronic Medical Records (EMR), as the core data of medical big data, has improved the efficiency of medical services in the process of sharing and use, but a series of data privacy leakage problems have emerged. Access control technology can ensure users' legitimate and appropriate access to data resources, but traditional access control has many limitations such as static and coarse-grained, which is difficult to meet in the dynamic and complex healthcare environment. We create a model to solve the privacy leakage problem of healthcare big data. The model utilizes the Latent Delicacy Allocation (LDA) topic model to quickly search documents and extract the topic probability distributions between the target patient and other patients' electronic medical records, which improves the efficiency of medical record document analysis; Calculating physician access similarity, we quantify physician access behavior to more accurately describe physician access behavior in the form of data. We combined the intuitionistic fuzzy theory with the trust evaluation method to create a novel trust evaluation method-- Intuitionistic Fuzzy Trust Evaluation Method, which can reflect the uncertainty characteristics that exist in the physician's diagnostic and treatment process when quantifying the trust of the access by using this method, and it is more flexible and practical, which enhances the accuracy of the quantification of the trust. Improving the traditional trust aggregation method by adding a time window and time decay function, the optimized method improves the accuracy of trust aggregation, and the experimental results are more in line with the actual law. We created a new intuitionistic fuzzy trust access control model using a relative entropy-based intuitionistic fuzzy clustering algorithm and a reward and punishment mechanism to achieve adaptive and dynamic access control for physician access behavior. Experiments show that the access control model based on intuitionistic fuzzy trust proposed in this article achieves 95% correctness in identifying user access behaviors and 94.29% correctness in identifying excessive user access behaviors. Experiments have proved that this model has obvious advantages over other models in the process of privacy protection of electronic medical record systems and has certain control effects.

Variable Gap MAG Welding Penetration Control Using Rough Set Knowledge Acquisition

The establishment of the correspondence between the important features of the topside weld pool and the penetration state is a key link in penetration control. This article presents a scheme of variable gap welding penetration control based on rough-fuzzy modeling. By utilizing the basic concepts of variable precision rough sets and conditional information entropy, relevant algorithms for attribute reduction and rule extraction in decision information systems are addressed. Raw data for modeling are obtained through current–gap combination experiments, and then rough-set-based knowledge acquisition algorithms are used to generate classification rules for the penetration state. Based on the correspondence between the important features of the topside pool and the penetration state, a fuzzy control model is established. Welding experiments under variable gaps are conducted to examine the proposed control scheme, and the results indicate that the weld has good penetration and meets the requirements of welding specifications.

Fuzzy logic control for watering system

A two-dimensional finite element (FEM) model was developed to simulate water propagation in soil during irrigation. The first dimension was water distribution depth in soil, and the second dimension was time. The developed model was tested by analyzing water distribution in a conventional (clock-controlled) irrigation model. The values in the conventional model were calculated based on the literature. The results were consistent with the results obtained from the model. In the next step, a fuzzy logic model for irrigation control was developed. The input variables were ambient temperature, soil moisture content and time of day (which is related to solar radiation and evapotranspiration), and the output variable was irrigation intensity. The fuzzy logic control (FLC) model was tested by simulating water distribution in soil and comparing water consumption in both models. The study demonstrated that the depth of the soil moisture sensor affected water use in the fuzzy logic-controlled irrigation system relative to the conventional model. Water consumption was reduced by around 12% when the soil moisture sensor was positioned at an optimal depth, but it increased by around 20% when sensor depth was not optimal. The extent to which the distribution of fuzzy variables affects irrigation performance was examined, and the analysis revealed that inadequate distribution of fuzzy variables in the irrigation control system can increase total water consumption by up to 38% relative to the conventional model. It can be concluded that a fuzzy logic-controlled irrigation system can reduce water consumption, but the system’s operating parameters should be always selected based on an analysis of local conditions to avoid an unintended increase in water use. A well-designed FLC can decrease water use in agriculture (thus contributing to rational management of scarce water resources), decrease energy consumption, and reduce the risk of crop pollution with contaminated groundwater.