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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]

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]

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]

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.)

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control 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]

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]

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]

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]

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]

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]

Restriction of water resources requires optimal use of agricultural water resources. In this regard, the use of new technology may increase irrigation efficiency. In arid and extremely arid regions such as Iran, the time and duration of irrigation is the key to achieving sustainable irrigation. Therefore, in this research, after constructing a garden in Iran, Karaj, Imam Khomeini Higher Education Center with an area of 0.05 ha, and installing equipment related to irrigation and fertilization, a fuzzy control system was designed to optimize water consumption and inputs, intelligent irrigation and fertilization system. The present study presents a practical solution based on artificial intelligence in which all stages of design and implementation are described. In this system, a microcontroller was first used to process and store data on soil moisture, ambient temperature and solar radiation. The fuzzy logic controller then takes these three inputs and based on the table of rules created for a specific product, produces the desired irrigation time and duration. Experimental results showed that, the developed system accurately compensates for the amount of water lost through evapotranspiration. It seems that the use of fuzzy control has a great impact on irrigation management and planning in the near future. [ABSTRACT FROM AUTHOR]

Every day, the number of newly confirmed cases of coronavirus (COVID-19) rises in many countries. It is critical to adjust policies and plans in order to investigate the relationships between the distributions of the spread of this virus in other countries. During this study, the intuitionistic fuzzy c-means (IFCM) clustering method is used to compare and cluster the distributions of COVID-19 spread in 62 countries. Using the IFCM clustering algorithm, the study aims to cluster the countries that use environmental, economic, social, health, and related measurements that affect disease spread to implement policies that regulate disease spread. As a result, countries that have similar factors can take proactive measures to address the pandemic. The data are obtained for 62 countries, and six different feature variables (factors associated with the spread of COVID-19) are determined. The data are obtained for 62 countries, and six variables with different characteristics (linked to the spread of COVID-19) are identified. In this study, the IFCM clustering algorithm is used to determine the dynamic behavior of COVID-19 based on real-world data for multiple countries and Turkey around the world. Data analysis is performed through MATLAB 2018a and R programs. The clustering results revealed that the distribution of dissemination in Brazil, India, and the United States was nearly identical and distinct from that of the 59 other countries. [ABSTRACT FROM AUTHOR]

This paper presents an analytical design of a fractional order fuzzy proportional integral plus derivative (FOFPI + D) controller. Artificial intelligence is incorporated into the controller with the help of a formula-based fuzzy logic system. The designed scheme combines fractional order fuzzy PI (FOFPI) and fractional order fuzzy D (FOFD) controller, derived from fundamental FOPID control law. The proposed scheme enjoys the linear structure of the FOPID controllers with non-linear gains that provide self-tuning control capability. The sufficient condition for stability of the closed-loop system is also established using the graphical approach. Performance of the proposed FOFPI + D, its integer order variant (FPI + D), and conventional controllers is examined for control of a highly non-linear and uncertain two-link robotic manipulator system. The optimum parameters of controllers are found by minimising aggregated control variation and error objective through non-dominated sorting genetic algorithm-II (NSGA-II). The comparison for trajectory tracking shows that FOFPI + D has the minimum integral absolute error (IAE) compared to other controllers. Further, rigorous performance investigations are performed to verify the robustness of designed controllers against parametric uncertainties, the varying boundary conditions of reference trajectory and disturbance rejection. It is concluded from the results that the proposed FOFPI + D controller exhibits superior performance. [ABSTRACT FROM AUTHOR]

Footprint of uncertainty (FOU) characteristics of interval type-2 membership functions (IT2-MFs) play an important role in the performance and robustness of interval type-2 fuzzy controllers (IT2-FCs). In literature, fixed FOU structures are used in almost all IT2-FC designs. In this study, an IT2-FC with dynamic FOU is proposed to provide high performance and robustness in the altitude control of a quadcopter. A proportional–derivative (PD) type FC with Takagi–Sugeno rule structure is used in the design procedure of the proposed controller. Additionally, input variables (error and derivative of error) are defined by using triangular IT2-MFs. To provide a dynamic FOU structure, the height of the lower MF (LMF) of each interval type-2 fuzzy set is defined as a function of the system error. In this way, FOU levels of IT2-MFs are adjusted dynamically since the height value of the LMF directly determines the FOU level of the IT2-MF. To evaluate the effectiveness of the proposed controller, comparison studies are performed under different conditions, including unmodeled measurement noises, an external disturbance, and a system parameter uncertainty. A classical PD, a type-1 fuzzy PD, and an interval type-2 fuzzy PD with fixed FOU controllers are used in the comparisons. The comparison results demonstrate that the proposed interval type-2 fuzzy PD controller with dynamic FOU exhibits better performance and more robustness than the other controllers. [Display omitted] • A type-2 fuzzy controller with dynamic footprint of uncertainty is introduced. • Dynamic FOU parameters defined as a function of the system error. • Dynamic FOU improves the performance and robustness of type-2 fuzzy controllers. • The proposed method can easily be used in real-time applications. [ABSTRACT FROM AUTHOR]

A machine learning based generalized neural network estimator (GRNNE) and Takagi‐Sugeno (T‐S) fuzzy control system is implemented to accelerate the functional performance indices of dynamic voltage restorer (DVR). The GRNNE predictive model is recommended for the fast estimation of the reference load voltage under the distorted power supply. The fruit fly optimization learning strategy is employed to optimize the weights and smoothing parameters for the extraction of the reference voltage signals as well as unit vectors, resulting in the required sinusoidal load component. The dynamics in the DC‐link voltage are optimized by the metaheuristic‐based gray wolf optimization algorithm. The coefficients of the adaptive controller are updated automatically to achieve the best fitted adaptive neuro‐fuzzy inference system predictive model with the least tracking voltage deviation error even in the presence of voltage disturbances. In comparison to classical techniques, the recommended neural‐based approach offers a faster convergence speed with fewer parameters to tune, shorter training time, and lower risks of local entrapment. The performance metrics such as mean square error, root mean square error, mean absolute error, mean absolute percent error, coefficients of correlational and determination (R and R2) are used to evaluate the efficacy of the proposed controller and hence enhance the DVR performance. Finally, the simulation results of the hybrid approach confirm that the NN‐based DVR estimator has proven its ability to alleviate voltage sensible issues at critical loads and outperform others in reducing power quality issues. [ABSTRACT FROM AUTHOR]

Automation and control systems are constantly evolving, using artificial intelligence techniques to implement new forms of control, such as fuzzy control, with advantages over classic control strategies, especially in non-linear systems. Water supply networks are complex systems with different operating configurations, uninterrupted operation requirements, equalization capacity and pressure control in the supply networks, and high reliability. In this sense, this work aims to develop a fuzzy pressure control system for a supply system with three possible operating configurations: a single motor pump, two motor pumps in series, or two motor pumps in parallel. For each configuration, an energy efficiency analysis was carried out according to the demand profile established in this case study. In order to validate the proposed methodology, an experimental water supply system was used, located in the Laboratory of Energy Efficiency and Hydraulics in Sanitation at the Federal University of Paraiba (LENHS/UFPB). [ABSTRACT FROM AUTHOR]

This paper presents the design of a fuzzy-controller-based ultra-high vacuum pressure control system and its performance evaluation for a sputter-ion vacuum pump used in the electron storage ring at the Synchrotron Light Research Institute (Public Organization) in Thailand. The production of synchrotron light requires advanced vacuum technology to maintain stability and prevent interference of electrons in an ultra-high vacuum pressure environment of about 10−9 Torr. The presence of heat and gas rupture from the pipe wall can affect the quality of the light in that area. The institute currently uses a sputter-ion vacuum pump which is costly and requires significant effort to quickly reduce pressure increases in the area. Maintaining stable vacuum pressure throughout electron motion is essential in order to ensure the quality of the light. This research demonstrates a procedure for evaluating the performance of a sputter-ion vacuum pump using a mathematical model generated by a neural network and Molflow+ software. The model is used to estimate the pumping speed of the vacuum pump and to design a fuzzy control system for the ultra-high vacuum system. The study also includes a leakage rate check for the vacuum system. [ABSTRACT FROM AUTHOR]

In the present paper, we focus on the reducibility of an almost-periodic linear Hamiltonian system... ...where J is an anti-symmetric symplectic matrix, A is a symmetric matrix, Q(t) is an analytic almostperiodic matrix with respect to t, and e is a parameter which is sufficiently small. Using some nonresonant and non-degeneracy conditions, rapidly convergent methods prove that, for most sufficiently small e, the Hamiltonian system is reducible to a constant coefficients Hamiltonian system through an almost-periodic symplectic transformation with similar frequencies as Q(t). At the end, an application to Schr?odinger equation is given. [ABSTRACT FROM AUTHOR]

To create various kinds of inequalities, the idea of convexity is essential. Convexity and integral inequality hence have a significant link. This study's goals are to introduce a new class of generalized convex fuzzy-interval-valued functions (convex FIVFs) which are known as (p, J)-convex FIVFs and to establish Jensen, Schur and Hermite-Hadamard type inequalities for (p, J)-convex FIVFs using fuzzy order relation. The Kulisch-Miranker order relation, which is based on interval space, is used to define this fuzzy order relation level-wise. Additionally, we have demonstrated that, as special examples, our conclusions encompass a sizable class of both new and well-known inequalities for (p, J)-convex FIVFs. We offer helpful examples that demonstrate the theory created in this study's application. These findings and various methods might point the way in new directions for modeling, interval-valued functions and fuzzy optimization issues. [ABSTRACT FROM AUTHOR]

The present paper is the result of a contemplative study of a multi-time control problem (MCP) by considering its associated equivalent auxiliary control problem (MCP)ς via the exact l1 penalty method. Further study reveals that the solution set of the considered problem and the auxiliary problem exhibits an equivalence under the KT-pseudoinvexity hypothesis. Moreover, the study is extended towards the saddle point defined for (MCP) to establish the relationship between the solution set of multi-time control problem (MCP) and its associated equivalent auxiliary control problem (MCP)ς. Finally, we present an illustrative application to authenticate the results presented in this paper. [ABSTRACT FROM AUTHOR]

This article begins with a dynamical analysis of the Permanent Magnet Synchronous Generator (PMSG) in a wind turbine system with quadratic nonlinearities. The dynamical behaviors of the PMSG are analyzed and examined using Poincare map, bifurcation model, and Lyapunov spectrum. Finally, an adaptive type-2 fuzzy controller is designed for different flow configurations of the PMSG. An analysis of the performance for the proposed approach is evaluated for effectiveness by simulating the PMSG. In addition, the proposed controller uses advantages of adaptive type-2 fuzzy controller in handling dynamic uncertainties to approximate unknown non-linear actions. [ABSTRACT FROM AUTHOR]