Mamdani Fuzzy Controller Research Articles

Pengungkapan Kinerja Lingkungan Instansi Pemerintah Daerah Melalui Laporan Keberlanjutan Berbasis Global Reporting Initiative

Laundry washing is a crucial process in daily life. Although automatic washing machines have simplified the washing process, utilizing advanced technology like fuzzy logic control can enhance the performance of smart washing machines to achieve optimal washing results. This research aims to implement fuzzy logic control using the Mamdani method on smart washing machines to improve efficiency and washing quality. The research methodology consists of several stages. Firstly, analyzing variables that affect the washing process, such as laundry load, fabric thickness, dirt level, and water temperature. Next, designing a Mamdani fuzzy logic control system by determining membership functions for each variable and creating fuzzy rules to link input and output variables. Lastly, evaluating washing results based on predefined input variables. The research findings demonstrate that implementing Mamdani fuzzy logic control on smart washing machines can significantly enhance washing quality and adaptively determine washing parameters to achieve cleaner washing outcomes. Therefore, integrating Mamdani fuzzy logic control into smart washing machines has the potential to improve overall washing performance. In conclusion, utilizing Mamdani fuzzy logic control on smart washing machines is an effective step towards enhancing efficiency and effectiveness in laundry washing. This research contributes to the development of more adaptive and environmentally-friendly smart washing machine technology and plays a crucial role in advancing fuzzy logic control technology for other smart household applications.

A soft robot tactile fingertip for grasping surface posture detection

Abstract The application field of robots is gradually extending from traditional industrial manufacturing to commercial services, medical care and other fields. Stable grasping is a necessary prerequisite for various complex robot application scenarios. The existing research on robot tactile sensor mainly focus on sensing tactile information, such as pressure or sliding. However, grasping surface posture as a tactile information that has a significant impact on the stability of grasping is often ignored. This study proposed a novel soft robot tactile fingertip that can not only detect the grasping force but also simultaneously detect the grasping surface posture. It is capable of recognizing 19 grasping surface postures in real-time using Mamdani fuzzy control method. The soft robot tactile fingertip has a simple structure and easy to fabricate at low cost. The application example of the two-finger mechanical hand using the soft tactile fingertip samples for stable grasping is demonstrated. This study shows that the soft robot tactile fingertip has great application prospects in the filed of robot intelligent grasping.

Fuzzy Logic Controllers Design for the Path Tracking of an Autonomous Coaxial Octorotor

Due to the essential characteristics and utility of coaxial octocopter vehicles in various real-time applications and many actual domains, this article aims to propose intelligent fuzzy logic controllers (FLC) for a six-degrees-of-freedom coaxial octorotor with a dynamic model. Using expert knowledge, the developed control structure is applied to track reference paths in the 3D environment with sufficient stability and effectiveness. The tested octorotor dynamic model is divided into six subsystems: altitude, pitch, roll, yaw, x position, and y position. Meanwhile, a Mamdani fuzzy inference controller is designed and applied for each subsystem to generate the appropriate control action. The simulation and the evolution tasks are done using a MATLAB dynamic model of the controlled process in order to perform and evaluate the proposed intelligent control structures of the 6 DOF octorotor model. The illustrated simulation results demonstrate the designed fuzzy controllers' effectiveness in motion stability and error tracking for different types of paths and all tested variables.

A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

AbstractNowadays, the use of fuel cells in various applications has become very widespread. Some of their most important applications are in electric vehicles (EVs) and local off‐grid power systems. An EV works as an electric motor that burns a mixture of fuel and gases to generate electricity. One of the challenges with fuel cells is the slow dynamic response to load power changes. In the past few decades, the modeling and control of DC‐DC converters have undergone extensive research and development. However, the error, settling time, and peak overshoot performance are not reduced by the robust nonlinear controller and current mode controller. To overcome these problems, quantile regressive extreme seeking cat swarm optimized Mamdani fuzzy PI controller (QRESCSO‐MFPIC) approach is developed. The goal of the QRESCSO‐MFPIC approach is to reduce the integral time absolute error (ITAE) for tuning the fuzzy PI controller. Initializing the fuzzy PI controller parameter is an input in the QRESCSO‐MFPIC approach. For every parameter value, the fitness function is determined and quantile regression analysis is carried out. The efficiency of the QRESCSO‐MFPIC approach is measured by evaluating the settling time and peak overshoot. Tuning of the PI controller is carried out with cat swarm optimization (CSO) and particle swarm optimization (PSO) based on the objective function of ITAE. The result analysis shows that the QRESCSO‐MFPIC approach improves the efficiency of an optimized Fuzzy PI controller compared to the existing methods. Peak overshoot reduction and settling time are improved through the QRESCSO‐MFPIC approach than the proportional integral particle swarm optimization controller (PI‐PSO).

Risk assessment in a rock slope stability analysis using a Mamdani fuzzy controller

Risk assessment in a rock slope stability analysis using a Mamdani fuzzy controller

Improved load frequency control in power systems with high penetration of wind farms using robust fuzzy controller

In highly penetrated wind power systems, similar to conventional units, wind farms must contribute to the frequency control task. This paper develops a model for load-frequency control (LFC) considering the participation of wind farms. Some well-known nonlinear physical constraints, i.e., time delay, generation rate constraint (GRC), and governor dead-band (GDB), which lead the system toward instability, are considered in the obtained model. Based on the obtained model, some control strategies are suggested to realize the participation of wind farms in the LFC task. First, the Mamdani fuzzy control approach is applied to improve the frequency behaviour of the power system in case of disturbance occurrence. However, this controller is unable to perform well against the aforementioned physical constraints. To solve this issue, a smart fuzzy controller is finally proposed to obtain maximum functionality against involved nonlinear constraints. To show the superiority of designed compensators, some simulations are carried out in the MATLAB/SIMULINK software to compare the performance of the suggested controller. Studies confirm the efficiency and high accuracy of proposed algorithms.

A fuzzy expert system for setting Brazilian highway speed limits

• A fuzzy expert system to set Brazilian highway speed limits was developed. • The system was developed using expert judgment and simulated highway scenarios. • The system was validated in case studies of Brazilian highway sections. • The methodology can be used to assist practitioners to set highway speed limits. Speed limits should provide a balance between safety and traffic flow. However, setting a speed limit generally involves selecting a base speed (e.g., operational speed, design speed) and adjusting it with several other variables. For example, in Brazil, the standard guidelines list a series of factors that influence speed limits, but they do not provide a clear methodology to choose a speed limit for a highway segment. Therefore, under current Brazilian practice, the decision-maker is faced with a subject decision that mostly relies on expert judgment. This paper proposes a fuzzy expert system for setting Brazilian highway speed limits. Six input variables are considered in the system. Membership functions and fuzzy rules were extracted from experts’ evaluations of simulated highway scenarios. The experts evaluated the scenarios using linguistic variables and proposed speed limits. Then, a Mamdani fuzzy controller was developed. The outputs of the controller were compared to the expert’s answers for the simulated highway scenarios. The system was further validated considering two case studies of highway sections in Brazil. Results showed that the fuzzy system is able to provide outputs that agree with the experts’ evaluations and with the existing speed limits. The fuzzy controller developed in this study can be used to assist practitioners in setting highway speed limits on Brazilian highways.

Synchronization and Optimal Operation of a 140 kVA Inverter in On-Grid Mode Using Mamdani Controllers in Cascade

This paper addresses the synchronization and operation of a 140 kVA inverter system connected to the main grid as part of a decentralized microgeneration system. The considerations for the supply of electrical energy stored in battery banks, mostly of photovoltaic origin, involve a study of the details of a rigid nonlinear system, which parallels the generation and distribution standards typical of hydroelectric and thermoelectric plants. Considering aspects related to power electronics operation, this paper presents both the modeling and the controlling aspects necessary to synchronize and ensure a stable operation of the microgeneration systems when connected to the main grid. Statistical processing was developed to guarantee synchronization between the systems without presenting electric shocks by simulating the magnetic link in asynchronous generators to meet this aim. The proposed model simulates the increase in power by a phase shift by maintaining a constant frequency based on a Chirp wave generator. The proposed process considers a generation power baseband operation. A Mamdani-type fuzzy proportional-integral controller is used to determine the power setpoint, which sets the Chirp generator phase shift setpoint, which includes a Mamdani fuzzy proportional-type controller. Both controllers are connected in a cascade. The applied correlational technique to achieve the synthesis of the sinusoid and the synchronization presented optimal performance when using 17 samples per signal period. The design of the transformer primarily, guaranteed a phase shift of −4.3018°, allowed for a THD below 2.75%.

Robotic Arm Position Control using Mamdani Fuzzy Logic on Arduino Microcontroller

A robotic arm is the most often used robot in manufacturing to perform the same task accurately again and over again in a controlled environment. However, direct positioning control of the robotic arm is always inaccurate as it does not consider its position in the external environment. Therefore, this work implements the Mamdani fuzzy logic in the position control of the 6 DOF robotic arm to improve the accuracy and movement of each joint at this manipulator. The implemented fuzzy logic Mamdani inference system is done in MATLAB and finally converted to the C language to accommodate the main microcontroller, Arduino UNO environment. There are 6 servo motors controlled by this approach and the result is compared with the conventional method. It is found that the Mamdani fuzzy logic controller has an average error of 0.67% while the direct control method has an average error of 2.33%. Based on this result, it shows that the use of the Mamdani fuzzy logic increases the accuracy and reduces the swaying movement after stopping the robotic arm movement.

Closed-loop modulation of model parkinsonian beta oscillations based on CAR-fuzzy control algorithm.

Closed-loop deep brain stimulation (DBS) can apply on-demand stimulation based on the feedback signal (e.g. beta band oscillation), which is deemed to lower side effects of clinically used open-loop DBS. To facilitate the application of model-based closed-loop DBS in clinical, studies must consider state variations, e.g., variation of desired signal with different movement conditions and variation of model parameters with time. This paper proposes to use the controlled autoregressive (CAR)-fuzzy control algorithm to modulate the pathological beta band (13-35Hz) oscillation of a basal ganglia-cortex-thalamus model. The CAR model is used to identify the relationship between DBS frequency parameter and beta oscillation power. Then the error between the one-step-ahead predicted beta power of CAR model and the desired value is innovatively used as the input of fuzzy controller to calculate the next step stimulation frequency. Compared with 130Hz open-loop DBS, the proposed closed-loop DBS method could lower the mean stimulation frequency to 74.04Hz with similar beta oscillation suppression performance. The Mamdani fuzzy controller is selected because which could establish fuzzy controller rules according to human operation experience. Adding prediction module to closed-loop control improves the accuracy of fuzzy control, compared with proportional-integral control and fuzzy control, the proposed CAR-fuzzy control algorithm has higher tracking reliability, response speed and robustness.

Induction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller

The substantial rise in the demand for electric vehicles (EVs) has emphasized an environment-friendly and intelligent design for speed control strategies. In this paper, a Mamdani fuzzy logic controller (MFLC) was proposed to vigorously control the speed of EVs at discrete levels. MFLC member functions (MFs) are tuned for EVs operating at three different speed modes (40, 60, and 80 km/h). The proposed speed controller operation for the speed tracking of EVs was designed and tested in MATLAB (Simulink) environment. The proposed speed controller validated a remarkable improvement in dynamic speed control compared with existing P-I, FLC, Fuzzy FOPID (ACO), Fuzzy FOPID (GA), and Fuzzy FOPID (PSO) controllers. Its stability under a user-defined drive pattern is also observed. In this proposed work, the speed controller highlights the better tracking of user-defined speed response compared to the conventional aforementioned controllers. Moreover, the speed tracking of the designed model was tested for robustness against speed transients at predefined time instants, respectively. The comparison suggests that the MFLC model removes overshoot and significantly reduces the steady-state time.

Development, experimental validation, and comparison of interval type-2 Mamdani fuzzy PID controllers with different footprints of uncertainty

Interval Type-2 Fuzzy PID (IT2FPID) controllers use Interval Type-2 Fuzzy Sets (IT2FSs). The difference between Type-1 Fuzzy PID (T1FPID) controllers and IT2FPID controllers is the Footprint of Uncertainty (FOU) which occurs due to the use of IT2FSs. FOU gives an extra degree of freedom to the controller structure, which is capable of handling uncertainty in the plant. However, the effect of FOU on the analytical structures of the IT2FPID controllers is not yet explored when uniformly/non-uniformly distributed multiple IT2FSs are used on the input side and uniformly/non-uniformly distributed multiple singleton fuzzy sets are considered on the output side of the controller. In this paper, controllers with three different types of FOUs and two different defuzzification methods are considered, and their analytical structures are derived. The suitability of the controllers is examined, and the properties of the suitable controllers are explored. Effects of FOU on the nonlinearity, gain, and computational aspects of the suitable controllers are studied. The applicability of proposed IT2FPID controllers is shown through simulation and real-time studies. The effectiveness of the proposed controllers is depicted by comparing their performances with that of the recently developed Integer Order Fuzzy PID (IOFPID) and Fractional Order Fuzzy PID (FOFPID) controllers.

Компаративна анализа на различни алгоритми за управување на поле од хелиостати

This study presents the use of various algorithms for control of a field of heliostats, through which a thermal power plant with concentrated solar energy is controlled. The design of the control algorithms consists of several steps. First, the Sun tracking alghorithm is presented for a specific location with an accuracy of ± 0.0003°. In order to obtain the mathematical model of the system, the real system is identified according to the gray box and the least-square method. The data used to identify the system is generated by step excitation on the real system, for a specific sampling period. The resulting mathematical model is used to design and simulate a continuous and discrete Proportional-Integral-Derivative (PID) controller, Mamdani and Sugeno fuzzy logic controllers, as well as ANFIS based fuzzy logic controller. The results of the applied controllers are analyzed and compared, based on the output overshoot, the rise and settling time. It can be concluded that we got best results (least settling time and the least overshoot) when fuzzy logic controller with ANFIS was used, while in terms of speed and rise time, the best results were obtained when discrete PID control algorithm was used. The study is extension of the work given in [23].

DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

Autonomous omnidirectional mobile robot navigation based on hierarchical fuzzy systems

PurposeThis paper aims to propose a one-layer Mamdani hierarchical fuzzy system (HFS) to navigate autonomously an omnidirectional mobile robot to a target with a desired angle in unstructured environment. To avoid collision with unknown obstacles, Mamdani limpid hierarchical fuzzy systems (LHFS) are developed based on infrared sensors information and providing the appropriate linear speed controls.Design/methodology/approachThe one-layer Mamdani HFS scheme consists of three fuzzy logic units corresponding to each degree of freedom of the holonomic mobile robot. This structure makes it possible to navigate with an optimized number of rules. Mamdani LHFS for obstacle avoidance consists of a number of fuzzy logic units of low dimension connected in a hierarchical structure. Hence, Mamdani LHFS has the advantage of optimizing the number of fuzzy rules compared to a standard fuzzy controller. Based on sensors information inputs of the Mamdani LHFS, appropriate linear speed controls are generated to avoid collision with static obstacles.FindingsSimulation results are performed with MATLAB software in interaction with the environment test tool “Robotino Sim.” Experiments have been done on an omnidirectional mobile robot “Robotino.” Simulation results show that the proposed approaches lead to satisfied performances in navigation between static obstacles to reach the target with a desired angle and have the advantage that the total number of fuzzy rules is greatly reduced. Experimental results prove the efficiency and the validity of the proposed approaches for the navigation problem and obstacle avoidance collisions.Originality/valueBy comparing simulation results of the proposed Mamdani HFS to another navigational controller, it was found that it provides better results in terms of path length in the same environment. Moreover, it has the advantage that the number of fuzzy rules is greatly reduced compared to a standard Mamdani fuzzy controller. The use of Mamdani LHFS in obstacle avoidance greatly reduces the number of involved fuzzy rules and overcomes the complexity of high dimensionality of the infrared sensors data information.

Lux and current analysis on lab-scale smart grid system using Mamdani fuzzy logic controller

The increasing need for electrical energy requires suppliers to innovate in developing electric distribution systems that are better in terms of quality and affordability. In its development, it is necessary to have a control that can combine the electricity network from renewable energy and the main network through voltage back-up or synchronization automatically. The purpose of this research is to create an innovative lux and current analysis on a lab-scale smart grid system using a fuzzy logic controller to control the main network, solar panel network and generator network to supply each other with lab-scale electrical energy. In the control, Mamdani fuzzy logic controller method is used as the basis for determining the smart grid system control problem solving by adjusting the current conditions on the main network and the light intensity conditions on the LDR sensor. Current conditions are classified in three conditions namely safe, warning, and trip. Meanwhile, the light intensity conditions are classified into three conditions namely dark, cloudy and bright. From the test results, the utility grid (PLN) is at active conditions when the load current is 0.4 A (safe) and light intensity is 1,167 Lux (dark). Then the PLN + PV condition is active when the load current is 1.37 (warning) and the light intensity is 8,680 lux (bright). Finally, the generator condition is active when the load current is 1.6 (trip) and the light intensity is 8,680 (bright). Based on the test results, it is known that the system can work to determine which source is more efficient based on the parameters obtained.

A CENTER MANIFOLD THEORY-BASED APPROACH TO THE STABILITY ANALYSIS OF STATE FEEDBACK TAKAGI-SUGENO-KANG FUZZY CONTROL SYSTEMS

The aim of this paper is to propose a stability analysis approach based on the application of the center manifold theory and applied to state feedback Takagi-Sugeno-Kang fuzzy control systems. The approach is built upon a similar approach developed for Mamdani fuzzy controllers. It starts with a linearized mathematical model of the process that is accepted to belong to the family of single input second-order nonlinear systems which are linear with respect to the control signal. In addition, smooth right-hand terms of the state-space equations that model the processes are assumed. The paper includes the validation of the approach by application to stable state feedback Takagi-Sugeno-Kang fuzzy control system for the position control of an electro-hydraulic servo-system.

Design, Control, and Simulation of a Neonatal Incubator.

In this project, a fully functional incubator with precise control with respect to temperature, humidity, and airflow was developed and assessed. In parallel with the development of the incubator, a heuristic simulation was created to test and tune the Mamdani fuzzy logic controller. The controller was then applied to the incubator prototype.

Intelligent Control Systems and Fuzzy Controllers. II. Trained Fuzzy Controllers, Fuzzy PID Controllers

The problems of control systems intellectualization are observed. The necessity of intellectualization of a wide range of systems and control methods is proved. The hierarchy of levels of intellectual control observed and comparison analysis of different artificial intelligence devices given. Importance of target setting’s automation problems’ solving in control systems is pointed out, as well as intellectualization of anthropocentric systems, including the ones based on fuzzy logic and case-based reasoning. The logical-linguistic, analytical, learned and PID fuzzy controllers are considered, based on fuzzy logics of Zadeh. An overview of the Mamdani-type controllers, controllers based on TS-model and the ANFIS architecture, using neural network structure is provided. The conditions of optimality and stability of control systems with Mamdani fuzzy controllers are analyzed. The Sugeno dynamic models and the ANFIS adaptive models and the methods of learning developed on the basis of fuzzy controllers are considered. The structure of a Mamdani fuzzy controller and its implementation by means of the Simulink is described. An example of application of Simulink to determine the optimal parameters of a fuzzy controller is shown. The examples of the fuzzy controllers use are given.

Comparative Assessment of Control Strategies Used in a Hybrid AC-DC Micro-Grid Consisting of Composite Energy Sources

In the present paper, comparative assessment of two important available intelligent control strategies namely the Mamdani fuzzy logic controller and the T–S fuzzy controller used for DC-link voltage control in DC micro-grid is carried out, first. Then, a new inverter control technique is proposed to maintain the inverter voltage as well as the DC-link voltage in a hybrid AC-DC micro-grid. The DC micro-grid under study comprises of photovoltaic system, permanent magnet synchronous generator-based wind energy system, battery energy storage system, fuel cell, electrolyzer and DC loads. The complete DC Micro-grid along with its correlated control algorithms are examined and accessed through MATLAB/Simulink environment.