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This work studies the H ∞ performance of T-S fuzzy networked control systems under denial-of-service (DoS) attacks. First, the weight coefficient is introduced to measure the damage to the control signal caused by DoS attacks. Then, novel looped Lyapunov-Krasovskii functions are constructed based on the fuzzy membership function, and the nonlinear problem of the system is considered under the premise of reducing the initial condition constraints. Next, the issue of finding the suitable sampling period is transformed into an optimization problem of finding the optimal sampling period to reduce sampling times. An intelligent sampling controller is designed to ensure the asymptotic stability of the system. Finally, the effectiveness of the proposed method is verified with a truck trailer model. [ABSTRACT FROM AUTHOR]

The fault tolerant control (FTC) problem based on rational entropy performance criteria is researched for fuzzy descriptor stochastic distribution networked control (SDNC) systems with packet dropout. The independent Bernoulli distribution is employed to describe the packet dropout in the feedback channel. The static model and the dynamic model of descriptor SDNC systems are built up by the rational square‐root fuzzy logic model (FLM) and the T‐S fuzzy model, respectively. When the given output probability density function (PDF) is not known, the minimization of output randomness becomes an important control target. First, the unknown fault is estimated by developing the fuzzy fault estimation observer. Then, the fault tolerant controller with the fault compensation is designed so that the output of the descriptor SDNC system remains with the minimum uncertainty after the fault occurs. A simulation example is supplied to prove the proposed scheme. [ABSTRACT FROM AUTHOR]

Copyright of Riwayat: Educational Journal of History & Humanities is the property of Riwayat: Educational Journal of History & Humanities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fuzzy implications, as a generalization of the classical implication, are not only required in fuzzy logic systems and fuzzy control but also have an important effect on solving fuzzy relational equations, fuzzy mathematical morphology, image processing and etc. Therefore, it is necessary for us to investigate multiple types of fuzzy implications on different truth values sets. In this paper, we devote to propose the Q L -(operators) implications derived from quasi-overlap (quasi-grouping) functions and negations on bounded lattices. Firstly, we exactly investigate some desirable properties of Q L -operators. Afterwards, we provide a necessary and sufficient condition along with a sufficient condition for the Q L -operator to be a Q L -implication and study various prime properties of Q L -implications. Moreover, we show the relationship between Q L -implications and L-automorphisms on bounded lattices. Finally, we consider the Q L -implications to analyze their intersection with I G , N -implications derived from quasi-grouping functions and negations on bounded lattices. [ABSTRACT FROM AUTHOR]

The assembly line balancing problem is always explored using the assumption that the processing ability of each workstation is constant. However, the initial workload balance can be easily broken by the changing processing condition of the machines, due to degradation. In the context of Industry 4.0, real-time information related to the machine health state is available. The aim is to improve the performance of the assembly process by making full use of the obtained real-time information. This research is the first exploration of real-time assembly line balancing with the changing health states of machines and the trigger point of adjustments to the assembly line. In this study, a fuzzy control system is developed to determine when to re-balance the assembly line and how to adjust the production rates to smooth the workloads of the workstations. The numerical results show that the assembly line with the proposed fuzzy control system satisfies the demand for most cases, and achieves higher utilisation of machines and lower buffer levels. Therefore, the real-time information brought by Industry 4.0 can be used to improve the performance of an assembly line. [ABSTRACT FROM AUTHOR]

It is known that in crowded environments such as educational institutions and workplaces, keeping indoor air quality and climate within certain limits contributes to success and production. For this purpose, a system has been developed to ensure air quality well-being in working environments. In our study, the Arduino processor managed by the fuzzy logic control system (FLC) starts to work with the trigger of the motion sensor HC-SR501. The inputs of the FLC system are defined as LM-35 sensor for temperature, DHT-11 for humidity, MQ-135 for air quality, MQ-9 sensor for CO and explosive gas. The designed system evaluates the instantaneous data obtained from the fuzzy logic system rule base and decides which of the output air filter, heater and alarm systems will operate at what speed. In order to increase system efficiency, fuzzy logic input membership values are optimized by genetic algorithm. [ABSTRACT FROM AUTHOR]

The expansive growth of information on the Internet has led to new developments in computer vision technology and image processing techniques. Since stone inscriptions are subject to erosion and polishing by the external environment for years, it is difficult to extract image and text information. In this study, the fuzzy control theory is combined with edge detection technology for image edge detection. Firstly, a suitable fuzzy rule and affiliation function are set, then a fuzzy control system is used to extract and detect the image edge information, and then a fuzzy logic rule-based edge detection algorithm is proposed to detect the inscription images. To test the performance of the algorithm, the detection effect of the image is first analyzed from a subjective perspective. The experimental results show that the proposed algorithm has better edge detection for both inscription and lena images, with better noise suppression without excessive distortion, and clearer inscription images. The proposed algorithm has the lowest MSE value of 41.26 when the detection object is the lena image b, and the highest PSNR value of 33.84 when the detection object is the lena image a. The proposed algorithm has the lowest MSE value of 41.26 when the detection object is the lena image b, and the highest PSNR value of 41.26 when the detection object is the lena image b. The proposed algorithm has the highest PSNR value of 33.84 when the detection object is the lena image b. In summary, the analysis of both subjective and objective indicators shows that the inscription image processing algorithm used in this paper has better processing effect, and the processed images become clearer with less distortion, which is helpful for both inscription image and text extraction. [ABSTRACT FROM AUTHOR]

The most important resource in navigational safety for marine vehicles is radar information. The information from the radar is read continuously by an operator and the navigation process is tried to be carried out safely. In navigational safety, it is very important that the operator does not misclassify for object detection. In this study, for the first time in the literature, a fuzzy logic-based classifier has been created for navigational safety in the light of information obtained from radar with artificial intelligence. The classifier is designed to detect vessels, land, buoys and unidentified objects. Since there is no data set presented in the literature, fuzzy logic model, which is an expert-based artificial intelligence method, has been preferred. On the other hand, in order to test the performance of the model, a dataset is proposed with the algorithm designed synthetically from the help of expert experiences. As a result of the analysis, it has been shown that the fuzzy model can detect objects successfully. Moreover, it is predicted that this model will play a key role in the design of intelligent radar systems in the future. [ABSTRACT FROM AUTHOR]

Sugarcane bagasse is a cheap feedstock for one of the most significant biofuel technologies currently being developed: second-generation ethanol (E2G). Most studies regarding E2G production investigate ways to improve the efficiency of this technology. However, studies about its control are still very sparse. In this work, we tried to partially fulfill this gap, addressing the control of a multiple-effect evaporation system of a sugarcane biorefinery. A feedforward fuzzy controller was proposed with membership functions based on a dynamic phenomenological model developed in Environment for Modeling, Simulation, and Optimization (EMSO). This software was also used to carry out simulations to evaluate the disturbance rejection performance. Inference tools based on neural networks and proportional–integral (PI) controllers were also proposed as support to the Fuzzy control system. The tests showed that the Fuzzy scheme outperformed traditional proportional–integral–-derivative (PID) controllers, settling on average 66% faster with a 72% reduction of integral time absolute error (ITAE). [ABSTRACT FROM AUTHOR]

A well-designed energy management system plays a crucial role in increasing fuel efficiency and reducing polluting emissions in dual-power hybrid electric vehicles (HEVs), which are an intermediate stage in the transition from combustion engine vehicles to fully electric vehicles. Despite many studies to optimize energy management, innovative ideas are needed to ensure the most appropriate energy use according to changing road, vehicle, and driver types. For this purpose, we developed a data-driven method to construct a stochastic energy management system, considering realistic uncertainties. We have demonstrated that an HEV can be used more efficiently with an appropriate energy management strategy depending on the road type and driving style. We collected and analyzed 38 thousand km of real driving data with nine different drivers. We transformed these data into meaningful information with a comprehensive data processing methodology and then classified driving styles according to these data using data mining methods. The classification algorithm we designed predicted driving style for three different roads with an average success rate of 95%. We achieved better fuel and emission values with a fuzzy logic-based energy management system that we designed according to the driving style determined by our classification algorithm. The fuzzy controller we developed achieved fuel improvements of up to 7% on the motorway, 9% on the urban road, and 16% on the residential district, based on real driving data results. Although there is a trade-off between fuel and pollutant emissions, our proposed system has also produced significant improvements in harmful emissions. Our results can be used as an inspiration and guide in the studies of improving fuel and emissions in HEVs. [ABSTRACT FROM AUTHOR]

The research on the self-propelled electric tiller is vital for further improving the quality and efficiency of greenhouse rotary tillage operation, reducing the work intensity and operation risk of operators, and achieving environmentally friendly characteristics. Most of the existing self-propelled tillers rely on manual adjustment of the tillage depth. Moreover, the consistency and stability of the tillage depth are difficult to guarantee. In this study, the automatic control method of tillage depth of a self-propelled electric tiller is investigated. A method of applying the fuzzy PID (Proportional Integral Derivative) control method to the tillage depth adjustment system of a tiller is also proposed to realize automatic control. The system uses the real-time detection of the resistance sensor and angle sensor. The controller runs the electronically controlled hydraulic system to adjust the force and position comprehensively. The fuzzy control algorithm is used in the operation error control to realize the double-parameter control of the tillage depth. The simulation and experimental verification of the system are conducted. Results show that the control system applying fuzzy PID can improve the soil breaking rate by 3% in the operation process based on reducing the stability variation of tillage depth by 24%. The control strategy can reach the set value of tillage depth quickly and accurately. It can also meet the requirement of tillage depth consistency during the operation. [ABSTRACT FROM AUTHOR]

Inertial sensors can serve as inertial references for space missions and require charge management systems to maintain their on-orbit performance. To achieve non-contact charge management through UV discharge, effective control strategies are necessary to improve the optical power output performances of UV light sources while accurately modeling their operating characteristics. This paper proposes a low-power photo-electro-thermal model for widely used AlGaN-based UV LEDs, which comprehensively considers the interaction of optical, electrical, and thermal characteristics of UV LEDs during low-power operations. Based on this model, an optical power control system utilizing a fuzzy adaptive PID controller is constructed, in which a switch is introduced to coordinate the working state of the controller. Thus, the steady-state performance is effectively improved while ensuring dynamic performance. The results show that the proposed model has an average prediction error of 5.8 nW during steady-state operations, and the fuzzy adaptive PID controller with a switch can reduce the fluctuation of light output to 0.67 nW during a single discharge task, meeting the charge management requirements of high-precision inertial sensors. [ABSTRACT FROM AUTHOR]

Given the problems of targeted variable deep application of liquid fertilizer in the field, such as low precision, inaccurate fertilization amount, and poor fertilization effect, a targeted variable fertilization control system of liquid fertilizer based on a fuzzy PID algorithm was designed in this study to realize the combination of precise variable fertilization technology and targeted deep-fertilization technology. Specifically, the fertilization equipment and adaptive fuzzy PID control strategy of targeted variable fertilization were designed first. Then, the mathematical model of the targeted variable fertilization control system of liquid fertilizer was established following the requirements of intertillage and fertilization of corn crops. Afterward, the response time and overshoot of the control system were simulated through the Simulink tool of MATLAB software, in which the fuzzy PID control and traditional PID control were compared. Then, the control effect of the targeted variable fertilization control system was verified through field experiments. The test results demonstrated that in the process of simulation analysis, the response time of the variable fertilization control system based on fuzzy PID control was shortened by nearly 5 s on average compared to the system based on traditional PID control, and the error was controlled within 10%. In the field test, the target rate of targeted variable fertilization equipment for liquid fertilizer reached more than 80%, and the control accuracy of the liquid fertilizer application amount also remained above 90%. Finally, the tracking experiment to check the fertilization effect proved that the targeted variable deep-fertilization method of liquid fertilizer could further improve the yield of maize crops under the premise of reducing the fertilization cost. The study provides a feasible solution for the method of precise variable fertilization combined with targeted fertilization. [ABSTRACT FROM AUTHOR]

The key technology for accelerating locomotives and enhancing their stability is vibration control for vehicles. This paper analyzes the characteristics of passive suspension, active suspension, and semi-active suspension, and comes to the conclusion that semi-active suspension should be the preferred control mode for high-speed train suspension system in China due to its advantages of low energy consumption, simple control, and good failure-oriented safety. The goal of this paper is to improve the ride comfort and running stability of rolling stock, as well as the performance of suspension system. Meanwhile, this paper applies fuzzy control theory to semiactive suspension control based on the characteristics of rolling stock suspension systems, designs a fuzzy control system in accordance with the fuzzy control principle, creates a semi-active suspension model to implement this control system, and implements the control simulation of semi-active suspension system in MATLAB environment. This paper also analyzes the condition monitoring data gathered by mechanical and electrical equipment while it is operating, with the goal of researching the detection of locomotive equipment health conditions. It then extracts characteristic parameters based on the condition monitoring data gathered by sensors at a specific time. The condition monitoring data and the health state of electromechanical equipment are mapped using the adaptive neuro-fuzzy inference system to monitor the health status of electromechanical equipment. According to the simulation results, the semi-active suspension fuzzy control may moderate the suspension's dynamic deformation fluctuation, lessen the wheels' dynamic load, and lower the acceleration of the car's body. The goal of this paper is to improve the performance of the semi-active suspension system by optimizing the membership function and fuzzy control rules of fuzzy controller. [ABSTRACT FROM AUTHOR]

With the development of mechatronics robot intelligence and autonomy, automatic obstacle avoidance and path planning has become the core of the current robot navigation system research. This research aims to solve the problems of complex path planning and poor parameter strain in ultrasonic array navigation systems. Firstly, the research status of the mobile robot is introduced, and a new embedded navigation system based on ultrasonic array technology is designed. Then, the fuzzy control algorithm is introduced, and a new Positive/Negative (P/N) local path planning algorithm based on fuzzy control is proposed. The normal rule is used to make the robot move toward the target point, in which the robot avoids obstacles through negative rules. The complexity of the fuzzy system is reduced through binary negative rules. Then, the precise output is obtained through the path decision formula to realize the local path planning system of the mechatronic robot. Finally, the simulation results verify the effectiveness of the improved algorithm. Based on the joint simulation experiment platform of Simulink and Robot Operating System (ROS), the excellent performance of the proposed embedded navigation system has been verified. The new P/N local path planning algorithm of fuzzy control can make the running track more accurate and smoother by optimizing the path. The new embedded navigation system can provide theoretical analysis and practical reference for developing an electromechanical robot navigation system. [ABSTRACT FROM AUTHOR]

In this paper, the observer design problem for a T-S fuzzy bilinear control system is investigated. First, an observer of Kalman type is designed to estimate the system states for the linear case. Then, some new sufficient conditions are derived to show the exponential convergence of the solutions of the error equation for fuzzy bilinear systems. Furthermore, we consider some uncertainties of the system that are bounded and satisfy a certain condition where an observer is designed. Moreover, an application to Van de Vusse system is given. [ABSTRACT FROM AUTHOR]

Automatic control systems are a new advanced system uses computers and hardware controllers to act as a driving system. Increasing in cars numbers while we have the same infrastructure caused a large traffic problem so we need automatic adaptive controllers to control the open and close tasks of traffic light system. Fuzzy Logic is a conversation from classical logic with values of 0 for false and 1 for true to a range of values between 0 and 1. This study aims to create an adaptive traffic control system using fuzzy logic and image processing, we have a camera on each traffic light to calculate the number of cars there and then we feed the number of cars to a fuzzy logic system to decide which traffic light must become on. [ABSTRACT FROM AUTHOR]

An interface between a DC supply and an electric vehicle's drive fed by an inverter is a bidirectional DC-DC converter. In this research, a topology for an electric vehicle based on an induction motor that integrates a high voltage gain bidirectional non-isolated DC/DC converter with a three-phase inverter is proposed. This study compares a bidirectional DC to DC converter inverter system controlled by fuzzy logic (FL), and fractional order proportional integral derivative (FOPID). The suggested converter runs in discontinuous-current mode (DCM), with all switches and diodes switching at zero current. It is possible to operate across a wide duty cycle range while maintaining high output voltage gain, low switching stress, minimal switching losses, and high efficiency. The proposed converter's size and weight are decreased so as to support a wide range of duty cycle operations, maintain lower voltage stress on all devices, ensure equal current sharing among inductors, are simple to control, and require a more compact inductor. The converter also uses a constant input current which offers a choice for various applications. MATLAB Simulink is used to construct, model, and simulate open loop system, closed loop FL and FOPID. The results of these simulations are then reported. The investigations show that FOPID controlled DC-DC converter performed better response. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of observer-based adaptive sliding mode control (SMC) for nonlinear Markov jump systems (MJSs), using a Takagi-Sugeno (T-S) fuzzy model with the premise variables of fuzzy rules depend on system state. Firstly, an integral sliding surface is designed based on fuzzy observer system; Secondly, sufficient conditions for stochastic stability and $ H_{\infty} $ perturbation attenuate level are provided for the obtained sliding mode dynamics and error systems using linear matrix inequality techniques. Furthermore, an adaptive SMC law is combined with the observed state variables to ensure finite time reachability of the sliding surface. Lastly, the theory is validated with simulations based on a practical example. [ABSTRACT FROM AUTHOR]

As the penetration rate of wind power systems is rising, which causes the overall system's inertia to decline, the power system's capacity to regulate frequency will be negatively affected. Therefore, this paper investigates the inertia control of doubly fed induction generation, and an energy storage system is installed in the wind farm to respond to the frequency deviation. First, a fuzzy control-based virtual inertia adaptive control strategy is presented. The goal of dynamic adjustment of the virtual inertia coefficient is realized by taking into account the uncertain factors of wind speed and frequency change rate. A recovery strategy based on the energy storage system's level of charge is employed to prevent overcharging and over-discharging of the battery. Then, a weight factor based on frequency deviation is introduced to combine the droop output of the energy storage system with the virtual inertia output of the doubly fed induction generation, and the joint output mode of the wind storage system is determined in each stage of primary frequency regulation. Finally, the simulation verification is performed using the wind storage system simulation model created by MATLAB. The comparison results with other control methods prove that the proposed method is effective. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of Brain Emotional Learning based adaptive Type-2 Fuzzy Systems for control of dynamical systems. The BEL controller belongs to the class of bio inspired controllers, as its architecture is based on limbic system of human brain and is capable of providing solutions for complex real time problems. In this work, dynamics of Brain Emotional Learning are used for the adaptation of membership functions in the design of Type-2 Fuzzy Logic Controllers. The stability of the overall system is analysed through Lyapunov Yakubovich's criteria. The proposed approach is validated on the benchmark system such as inverted pendulum, CSTR and Ship heading control through simulation and in real-time environment using OPAL RT OP5600. [ABSTRACT FROM AUTHOR]

This study aims to improve the performance of a pneumatic positioning system by designing a control system based on Fuzzy Fractional Order Proportional Integral Derivative (Fuzzy FOPID) controllers. The pneumatic system's mathematical model was obtained using a system identification approach, and the Fuzzy FOPID controller was optimized using a PSO algorithm to achieve a balance between performance and robustness. The control system's performance was compared to that of a Fuzzy PID controller through real-time experimental results, which showed that the former provided better rapidity, stability, and precision. The proposed control system was applied to a pneumatically actuated ball and beam (PABB) system, where a Fuzzy FOPID controller was used for the inner loop and another Fuzzy FOPID controller was used for the outer loop. The results demonstrated that the intelligent pneumatic actuator, when coupled with a Fuzzy FOPID controller, can accurately and robustly control the positioning of the ball and beam system. [ABSTRACT FROM AUTHOR]

Summary: This study proposes a high gain observer‐based filtered backstepping control scheme for nonlinear systems with unknown dynamics and dead‐zone constraint. Majority of the previously papers describe dead‐zone by the conventional certain models, assume that all state variables are available and invoke the backstepping or conventional dynamic surface control (CDSC) approaches for controller design. This work describes the dead‐zone by the fuzzy model, invokes radial basis function neural network (RBFNN) to model the unknown functions and proposes a RBFNN‐based high gain observer to estimate unmeasurable states. To avoid the complexity explosion in the backstepping approach and eliminate the sensitivity to the time‐constant of the first‐order filters in the CDSC, a nonlinear tracking differentiator (NTD) with finite time convergence property is used instead of the first‐order filters in the CDSC to obtain derivative of the virtual inputs. Moreover, the error compensation mechanism and auxiliary signal are proposed to eliminate the influence of the filtering error and input nonlinearity, respectively. Despite the fuzzy dead‐zone, the proposed scheme makes the closed‐loop signals uniformly ultimately bounded. In comparison with the existing results, (i) some assumptions like certain model of dead‐zone and full state measurement are removed, (ii) "explosion of complexity" and sensitivity to the time constant of the first‐order filters are eliminated, (iii) effect of the filtering error and input nonlinearity are compensated, (iv) number of adjustable parameters and online computational burden are decreased effectively. Simulation results on the two well‐known examples verify the effectiveness and applicability of the proposed new design technique. [ABSTRACT FROM AUTHOR]

This paper considers the control problem of T–S fuzzy systems with mixed time delays. Based on the time derivative data of the membership function of T–S fuzzy systems, fuzzy Lyapunov functions are designed by two methods, and observer-based switching controllers are obtained. One is to design fuzzy Lyapunov functions by loosening the requirement of positive quality of symmetric matrices. The other approach takes the controller's form into account. First, the form of the system model is adjusted by comparing each state value involved in the system with the state value of the controller. Then, the fuzzy Lyapunov function is designed using the difference value of the comparison. Both of the above observer-based switching controllers can stabilize the system. Examples are provided to demonstrate the effectiveness of the switching controllers proposed in this paper. [ABSTRACT FROM AUTHOR]

The conventional state-space form often leads to control design strategies and stability analysis techniques generally applicable to dynamical processes. Nevertheless, it may also lead to higher model complexity and loss of interpretability. Here, we skip this representation for nonlinear dynamical systems with high-order input derivatives and nonlinear input–output relationships. Specifically, we incorporate the principle of H ∞ design within an observer-based adaptive fuzzy controller to guarantee robust stabilization and trajectory tracking for such nonlinear systems. The proposed approach has four integral components. Firstly, zero-order Takagi–Sugeno fuzzy systems approximate nonlinear and uncertain functions by the estimated states of the observer. Secondly, the H ∞ control attenuates fuzzy approximation errors, observer errors, and environmental effects to a prescribed attenuation level. Thirdly, the adaptive laws and the H ∞ term are met with simple equations, avoiding the positive definite matrices in Lyapunov equations. Fourthly, a compensation term is added to ensure the stability of the closed-loop system. Fifthly, the Lyapunov theory guarantees the asymptotic stability of the overall system and the H ∞ tracking performance of the output. Finally, the proposed method is applied to two unknown nonlinear systems under disturbances, noises, packet loss, and asymmetric dead-zone. The first is a second-order spring-mass-damper trolley system, and the second is a third-order nonlinear system. Comparing the results with a recent competing controller reveals that the proposed approach improves transparency and lowers tunable parameters, fuzzy basis functions dimension, the observation and tracking errors, the consumed energies, and the settling times. [ABSTRACT FROM AUTHOR]

A fuzzy fractional-order PID control algorithm for a general type industrial temperature control system is proposed in this paper. In order to improve the production quality and controlled model accuracy, a fractional-order elementary system is used to describe the temperature control process. The gain coefficients of the proposed fractional-order PID controller is updated online based on a set of fractional-order fuzzy rules which are defined by Mittag–Leffler functions and follow fat-tailed distributions. Therefore, the proposed controller parameters could be auto-tuned according to model uncertainties, noise disturbance, random delay, and etc. Examples of the studied temperature control systems are shown to verify the effectiveness of the proposed controller. The superiority of fractional calculus is fully explored in the presented control methodology. The controlled temperature profile with the proposed algorithm could realize more satisfactory dynamic performance, better robustness respect to environment changes caused by internal and external disturbance. [ABSTRACT FROM AUTHOR]

To enhance the ride comfort and improve the road friendliness of the heavy truck, based on the dynamic model of the heavy truck, the semi-active suspension system of the vehicle is proposed and controlled based on the fuzzy logic control and Matlab/Simulink software. The efficiency of the semi-active suspension system is then evaluated based on the indexes of the root mean square acceleration of the driver's seat, the root mean square acceleration of the cab's pitching angle, and the dynamic load coefficient of the wheel axles. The results show that with the semi-active suspension system of the heavy truck controlled by the fuzzy logic control, the acceleration responses of the heavy truck and the dynamic forces of the wheel axles are greatly reduced in comparison with the passive suspension system under various operating conditions of the loads and speeds. Especially, with the semi-active suspension system controlled by the fuzzy logic control, the root mean square accelerations of the driver's seat and cab pitch angle; and the dynamic load coefficient at 2nd axle of the wheel are clearly reduced by 23.7 %, 27.2 %, and 20.9 % in comparison with the passive suspension system, respectively. Thus, both ride comfort and road friendliness of the heavy truck are improved by the semi-active suspension system. In addition, the vehicle load insignificantly affects ride comfort. However, it greatly affects the road damage, especially with the half load condition of the vehicle. Thus, to improve road friendliness, the full load condition of the vehicle should be used. [ABSTRACT FROM AUTHOR]

We all wash our clothes on a daily basis. Traditionally, washing has been done by hand. However, today's technology has advanced greatly, and all manual labor has been replaced by machines. The washing machine is one such invention that allows consumers to conserve time, energy, and water. A fuzzy logic control system has been designed in response to human needs. A form of reasoning system known as fuzzy logic justifies YES or NO dependent on the input. These days, artificial intelligence is primarily used in automated products to mimic human thought. Many of the devices we use every day, including washing machines, air conditioners, satellites, unmanned aerial aircraft, traffic control systems, transmission systems, anti-lock brake systems, etc., utilize fuzzy logic problems. Python provides a straightforward answer to the fuzzy logic issue for the washing machine context. Until now, MATLAB was used to construct fuzzy logic problems for washing machines. However, Python logic is used in this, which mitigates the drawbacks of fuzzy logic in MATLAB. The type of clothing, level of grime, and load of clothing are the inputs for this procedure, and the wash time, RPM, dry time, and temperature are the outputs. This goal is used to cut down on the amount of time, energy, and water needed to wash clothes. The outcome of this simulation demonstrates that this washing machine offers a high-quality wash. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A fuzzy control system is a mathematical framework that evaluates analog input data in terms of logical variables with continuous values ranging from 0 to 1. From the 1970s on, fuzzy notions have exploded in popularity across all fields. Fuzzy logic that contains fuzzy values, fuzzy variables, and fuzzy sets is frequently used by engineers, statisticians, and programmers to describe vague concepts mathematically. In recent years, researchers have begun to investigate fuzzy systems in deep neural networks. In our study, the use of fuzzy inputs and the use of categorical inputs in classification problems were compared with and without dimension reduction. For the combination of four different datasets and three different encoder pairs, the accuracy using fuzzy values was higher than the accuracy using categorical values, and a 4.54656% average increase in accuracy value is maintained. For big-data analysis, in critical fields like the medical field, even a tiny gain in accuracy can make a big difference in people's lives. I hope that the findings will guide researchers to consider the fuzzy representation of the data in the data pre-processing part. [ABSTRACT FROM AUTHOR]

In this paper, based on Matlab/simulink, the PID vector frequency conversion control of permanent magnet synchronous motor has better drive control performance in the multi-stage centrifugal pump speed control system. The electric-hydraulic coupling system of centrifugal pump has a good evaluation and research function. In view of the low efficiency of the traditional PID control method and the poor adaptability to the change of working environment, a vector control method of permanent magnet synchronous motor based on adaptive and self-adjusting fuzzy control principle is proposed for the speed regulation system of water pump. The simulation results show that the PMSM system controlled by the fuzzy PID controller is superior to the traditional PID control, and has better disturbance resistance, dynamic and steady performance. [ABSTRACT FROM AUTHOR]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a new type of energy with the advantages of high efficiency, clean and pollution-free, fuel cells have attracted the attention of many experts and scholars. The efficient utilization of fuel cells will certainly become the mainstay of energy transformation and environmental protection. However, fuel cells have low power density, soft electrical output characteristics, and significantly delayed response to sudden load changes. When fuel cells are used as power supply energy alone, the output voltage fluctuates greatly, and the power supply reliability could be higher. To increase the fuel cell's service life in real world applications, a DC converter and an appropriate auxiliary energy storage power supply are combined to form a fuel cell hybrid power supply system that makes efficient use of the auxiliary energy storage system's availability, enhances the power supply system's adaptability through dynamic reconfiguration, and provides better flexibility overall. This work proposes a method for managing the energy produced by an urban integrated power supply system that includes fuel cells, supercapacitors, and solar cells. Applying the IF-THEN rule of load power and the state of charge of the supercapacitors, the power balance is adjusted between the su-percapacitors, photovoltaic cells, and fuel cells according to the defined fuzzy logic control. The intermittent nature of solar power production and the erratic nature of fuel cell output may both be mitigated using this technique, allowing the load power to operate more reliably. The simulation results show that the control strategy adopted in this paper is able to not only meet the load requirements but also reasonably allocate the functional requirements and improve the working efficiency of the system, resulting in a clear optimization effect on the system's control. In this paper, we focus on the fuel cell hybrid power supply system design, and then we use the idea of fuzzy logic control energy management to build the structure of the fuzzy logic control system, design the fuzzy controller, determine the functions, and verify the solutions through simulation and experimentation. [ABSTRACT FROM AUTHOR]

Numerous cyberattacks on connected control systems are being reported every day. Such control systems are subject to hostile external attacks due to their communication system. Network security is vital because it protects sensitive information from cyber threats and preserves network operations and trustworthiness. Multiple safety solutions are implemented in strong and reliable network security plans to safeguard users and companies from spyware and cyber attacks, such as distributed denial of service attacks. A crucial component that must be conducted prior to any security implementation is a security analysis. Because cyberattack encounters in power control networks are currently limited, a comprehensive security evaluation approach for power control technology in communication networks is required. According to previous studies, the challenges of security evaluation include a power control process security assessment as well as the security level of every control phase. To address such issues, the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) based on multiple criteria decision-making (MCDM) is presented for a security risk assessment of the communication networks of energy management and control systems (EMCS). The methodology focuses on quantifying the security extent in each control step; in order to value the security vulnerability variables derived by the protection analysis model, an MCDM strategy incorporated as a TOPSIS is presented. Ultimately, the example of six communication networks of a power management system is modelled to conduct the security evaluation. The outcome validates the utility of the security evaluation. [ABSTRACT FROM AUTHOR]

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This paper is the second part of the study of a planar manipulator and this section presents the construction of a prototype manipulator. A fuzzy control system for the manipulator is described in detail. An experimental study was carried out on the positioning of the end effector of the manipulator and a program written in the Delphi 6 environment was proposed to calculate the position. Prototype tests were performed for transpose and follow-up control. Based on the experimental results, a control quality analysis was carried out. [ABSTRACT FROM AUTHOR]

To resolve the difficult control issue of intelligent new energy automobile in the process of vehicle control, the study starts from the vehicle motion mode and splits the vehicle control into two parts, transverse control and longitudinal control. Among them, the longitudinal speed control is controlled by an improved adaptive PID model, while the lateral angle control is dominated by an indistinct adaptive PID model, afterwards longitudinal speed control is combined with the lateral angle control for joint control. Finally, the study uses the actual lane simulation to verify the three perspectives of transverse control, longitudinal control and joint control respectively. It shows that the longitudinal control and lateral control are well realized with the improved adaptive PID model control effect at 8km, 15km and 20km per hour. Meanwhile, maximum trajectory error of the combined direction and transverse dominate is always lower than 0.5m at vehicle speeds of 8km/h, 16km/h and 20km/h in the integrated road environment and the combined longitudinal speed and transverse angle control performance is good in the curves. This shows that the model designed by the research can realize adaptive real-time precision control. [ABSTRACT FROM AUTHOR]

Aiming at solving the problems of response lag and lack of precision and stability in constant grinding force control of industrial robot belts, a constant force control strategy combining fuzzy control and proportion integration differentiation (PID) was proposed by analyzing the signal transmission process and the dynamic characteristics of the grinding mechanism. The simulation results showed that compared with the classical PID control strategy, the system adjustment time was shortened by 98. 7%, the overshoot was reduced by 5. 1%, and the control error was 0. 2%- 0. 5% when the system was stabilized. The optimized fuzzy control system had fast adjustment speeds, precise force control and stability. The experimental analysis of the surface morphology of the machined blade was carried out by the industrial robot abrasive grinding mechanism, and the correctness of the theoretical analysis and the effectiveness of the control strategy were verified. [ABSTRACT FROM AUTHOR]

This article presents a study on the fuzzy control of self-balancing, two-wheel-driven, simultaneous localization and mapping (SLAM)-based, unmanned systems for Agriculture 4.0 applications. The background highlights the need for precise and efficient navigation of unmanned vehicles in the field of agriculture. The purpose of this study is to develop a fuzzy control system that can enable self-balancing and accurate movement of unmanned vehicles in various terrains. The methods employed in this study include the design of a fuzzy control system and its implementation in a self-balancing, two-wheel-driven, SLAM-based, unmanned system. The main findings of the study show that the proposed fuzzy control system is effective in achieving accurate and stable movement of the unmanned system. The conclusions drawn from the study indicate that the use of fuzzy control systems can enhance the performance of unmanned systems in Agriculture 4.0 applications by enabling precise and efficient navigation. This study has significant implications for the development of autonomous agricultural systems, which can greatly improve efficiency and productivity in the agricultural sector. Fuzzy control was chosen due to its ability to handle uncertainty and imprecision in real-world applications. [ABSTRACT FROM AUTHOR]

This paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one initial fuzzy rule. The rules are then continuously updated and pruned in an online fashion to achieve the desired set point. For the phase of learning parameters, all adjustable parameters of the fuzzy system are tuned by using a sliding surface method, which is based on the gradient descent algorithm. This method is used to minimize the difference between the expected and actual signals. Our proposed control method is model-free and does not require prior knowledge of the plant's dynamics or constraints. Instead, data-driven control utilizes artificial intelligence-based techniques, such as fuzzy logic systems, to learn the dynamics of the system and adapt to changes in the system, and account for complex interactions between different components. A robustness term is incorporated into the control effort to deal with external disturbances in the system. The proposed technique is applied to regulate the dynamics of a mobile robot in the presence of multiple external disturbances, demonstrating the robustness of the proposed control systems. A rigorous comparative study with respect to three different controllers is performed, where the outcomes illustrate the superiority of the proposed learning method as evidenced by lower RMSE values and fewer fuzzy parameters. Lastly, stability analysis of the proposed control method is conducted using the Lyapunov stability theory. [ABSTRACT FROM AUTHOR]

There are many inevitable disruptive events, such as the COVID-19 pandemic, natural disasters and geopolitical conflicts, during the operation of the container port supply chain (CPSC). These events bring ship delays, port congestion and turnover inefficiency. In order to enhance the resilience of the CPSC, a modified two-stage CPSC system containing a container pretreatment system (CPS) and a container handling system (CHS) is built. A two-dimensional resilience index is designed to measure its affordability and recovery. An adaptive fuzzy double-feedback adjustment (AFDA) strategy is proposed to mitigate the disruptive effects and regulate its dynamicity. The AFDA strategy consists of the first-level fuzzy logic control system and the second-level adaptive fuzzy adjustment system. Simulations show the AFDA strategy outperforms the original system, PID, and two pipelines for improved dynamic response and augmented resilience. This study effectively supports the operations manager in determining the proper control policies and resilience management with respect to indeterminate container waiting delay and allocation delay due to disruptive effects. [ABSTRACT FROM AUTHOR]

In dynamic outdoor environments characterized by turbulent airflow and intermittent odor plumes, robotic odor plume tracking remains challenging, because existing algorithms heavily rely on manually tuning or learning from expert experience, which are hard to implement in an unknown environment. In this paper, a multi-continuous-output Takagi–Sugeno–Kang fuzzy system was designed and tuned with reinforcement learning to solve the robotic odor source localization problem in dynamic odor plumes. Based on the Lévy Taxis plume tracking controller, the proposed fuzzy system determined the parameters of the controller based on the robot's observation and guided the robot to turn and move towards the odor source at each searching step. The trained fuzzy system was tested in simulated filament-based odor plumes dispersed by a changing wind field. The results showed that the performance of the proposed fuzzy system-based controller trained with reinforcement learning can achieve a similar success rate and higher efficiency compared with a manually tuned and well-designed fuzzy system-based controller. The fuzzy system-based plume tracking controller was also validated through real robotic experiments. [ABSTRACT FROM AUTHOR]

One of the main research areas in the field of musical human–AI interactivity is how to incorporate expressiveness into interactive digital musical instruments (DMIs). In this study we analyzed gestures rooted in expressiveness by using AI techniques that can enhance the mapping stage of multitouch DMIs. This approach not only considers the geometric information of various gestures but also incorporates expressiveness, which is a crucial element of musicality. Our focus is specifically on multitouch DMIs, and we use expressive descriptors and a fuzzy logic model to mathematically analyze performers' finger movements. By incorporating commonly used features from the literature and adapting some of Rudolf Laban's descriptors—originally intended for full-body analysis—to finger-based multitouch systems, we aim to enrich the mapping process. To achieve this, we developed an AI algorithm based on a fuzzy control system that takes these descriptors as inputs and maps them to synthesis variables. This tool empowers DMI designers to define their own mapping rules based on expressive gestural descriptions, using musical metaphors in a simple and intuitive way. Through a user evaluation, we demonstrate the effectiveness of our approach in capturing and representing gestural expressiveness in the case of multitouch DMIs. [ABSTRACT FROM AUTHOR]