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This article deals with the security sliding mode control (SMC) issue for T‐S fuzzy systems under random injection attacks. As a way to increase the communication efficiency of network control systems, the weighted try‐once‐discard (WTOD) protocol is utilized to decide whether the sensor nodes transmit or not. A compensating strategy is introduced in this controller side, based on which a fuzzy sliding mode controller depending on the scheduling signal is constructed. Then, the reachability of the given sliding surface together with the stochastic stability of the closed‐loop system are analyzed by incorporating the membership function, and the relaxed stability conditions are derived. Moreover, a GA‐based solving algorithm is proposed to search an optimized sliding matrix and improve the SMC performance. Finally, numerical simulation results are provided to verify the proposed SMC strategy. [ABSTRACT FROM AUTHOR]

This paper designs a preview repetitive control (PRC) policy for a class of Takagi–Sugeno (T–S) fuzzy systems with time-varying delay and external disturbances. First, the T–S fuzzy model is employed to represent a nonlinear plant, once the nonlinear plant is represented by a T–S fuzzy model, a fuzzy improved-equivalent-input-disturbance (IEID)-based PRC law can be designed to achieve better tracking performance and disturbance rejection. Next, based on a new equality constraint, we derive a continuous-discrete two-dimensional (2D) system that paves the way to solve the design problem of the IEID-based PRC law using linear matrix inequality (LMI) approach. Then, by solving LMIs, the gains of the controller and state observer can be obtained. Finally, a numerical example is included to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

An innovative technique for developing fuzzy preview tracking control nonlinear systems using Takagi‐Sugeno (T‐S) fuzzy models is discussed. The design considers robustness against state time‐changing latency, input saturation, and reference tracking. To achieve this, the T‐S fuzzy system was combined with time‐changing latency and input saturation to create an augmented error system, transforming the original fuzzy preview tracking control issue into a stability issue for the augmented error systems. Next, a new fuzzy preview tracking controller was developed by considering the T‐S fuzzy systems' states or outputs, a distinct integrator, and a previewed reference signal to solve the tracking control issue. For the augmented error system's asymptotic stability, new adequate conditions were derived by employing the fuzzy Lyapunov function, small gain theory, and linear matrix inequality (LMI) method. Finally, the proposed method's effectiveness was demonstrated using two numerical examples. [ABSTRACT FROM AUTHOR]

Summary: The problem of adaptive fuzzy fixed‐time tracking control based on a category of nonlinear systems with unmodeled dynamics and dynamic disturbances is investigated in this article. By introducing the novel fixed‐time dynamic signal, the unmodeled dynamics can be disposed of. On the basis of the fuzzy logic systems (FLSs) and adaptive backstepping technology, the disposing difficulty of the unknown nonlinear portions is reduced. Then, all signals in the closed‐loop system are ensured to be bounded under the fixed‐time Lyapunov stability theory. Simultaneously, the tracking error converges to a small neighborhood of the origin. Eventually, simulation consequences reveal the validity of the presented control method. [ABSTRACT FROM AUTHOR]

The heating source temperature of the vacuum belt system (VBD) is an important factor affecting the drying rate and the material quality. However, it has problems with large fluctuation, instability, and hysteresis due to interference from various factors, which increases the drying time and energy consumption. To address these issues, this study proposes fuzzy control and integral control synergistic (FCICS) control to realize temperature regulation of the VBD system, enhancing the performance and stability of the heating source. Simulations were conducted in Simulink, and an experimental verification was carried out based on the constructed experimental system. The results show that the FCICS control outperforms the conventional PID control in terms of material warming rate, temperature stability, and energy consumption, and the transient and stable state performance is improved. Specifically, the material warming rate increased by 15%, temperature stability improved by 20%, and energy consumption decreased by more than 1.74% with the FCICS control strategy. [ABSTRACT FROM AUTHOR]

There is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi‐area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi‐area MGs are regarded as cyber‐physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non‐linearities in the power system or server cyberattacks. [ABSTRACT FROM AUTHOR]

In order to improve the intelligent perception and adaptability of the 6G network, drones joined this challenge. For large-scale long-range Unmanned Aerial Vehicle (UAV), most of the time during normal flight belongs to fixed altitude flight. It is required to sail along the planned optimal path. Whether it can fly along the optimal path is mainly attributed to the tracking problem of horizontal flight trajectory. In order to minimize the UAV horizontal plane tracking error, it is necessary to consider the influence of many factors (such as strong winds, heavy rain, obstacles, etc.). Due to the complexity of High-Altitude environment, these disturbances are uncertain. In addition, there are some dynamic errors in the model of UAV control system, and these errors also have uncertainties. And, due to the change of global planning path coordinates, the control system needs to adjust the set value in real time during AUV horizontal trajectory tracking, and the conventional control algorithm is difficult to meet the requirements. Therefore, firstly, the influence of prediction uncertainty of grey prediction on AUV horizontal track tracking control is used; Then the grey prediction is improved according to the practical application; Ultimately, the control law is designed by combining the grey prediction with the control method. Finally, the grey prediction fuzzy adaptive PID method of UAV flight control is applied to the planned path simulation, and good control results are obtained. [ABSTRACT FROM AUTHOR]

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

The focus of this study is to investigate the critical matter of voltage sags in power systems, which have a substantial adverse effect on both the functionality of equipment and the quality of power. Central to this investigation is the integration of hydrogen fuel cells and a D-STATCOM system, which is suggested as a substitute approach for mitigating voltage fluctuations. The hydrogen fuel cell serves as the pivotal component, renowned for its ability to significantly improve power quality through the efficient mitigation of voltage sag concerns. The paper provides a comprehensive analysis of the contribution that hydrogen fuel cell integration with D-STATCOM makes to the reliability of power systems. A systematic methodology is employed, supported by simulations and modeling, to emphasize the critical significance of hydrogen fuel cells in surmounting obstacles related to voltage injection and enhancing power quality. By utilizing a Type-3 Fuzzy system, the research evaluates the dependability of the control system and proposed model. The effectiveness of this method, which is focused on hydrogen fuel cells, is verified by means of MATLAB simulations, which demonstrate its superior performance in comparison to conventional PI and ANFIS controllers. • Integrates hydrogen fuel cells with D-STATCOM for improved power quality. • Achieves notable reduction in voltage sags in power systems. • Enhances power system reliability and stability. • Employs Type-3 Fuzzy system for advanced control. • Validated by MATLAB simulations, outperforming traditional controllers. [ABSTRACT FROM AUTHOR]

This article addresses the problem of designing an event‐triggered fault detector within a networked nonlinear system described by a Takagi–Sugeno (T–S) fuzzy model, accounting for time‐varying delays and denial‐of‐service (DoS) attacks characterized by a binary Markov chain. Our focus is on constructing an event‐triggered residual system, which aims to detect malfunctions in the original fuzzy system while optimizing communication resource allocation. Specifically, it is tailored to combat DoS attacks in the event generator‐to‐fault detector channel and mitigate network‐induced time‐varying delays. Utilizing the Lyapunov–Krasovskii function approach, we formulate linear matrix inequalities to establish the necessary conditions ensuring the asymptotic mean‐square stability of the residual system, meeting a predefined H∞$$ {H}_{\infty } $$ performance criterion. Finally, the simulation results are presented to validate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Control technologies are of great importance in the rapidly developing electric vehicle industry. Control systems, especially in electric vehicles with more than one drive system or energy source, are at the top of the issues focused on to achieve low fuel and emission values, to provide adequate performance, and not to compromise on safety. Fuzzy logic controller (FLC) based systems are among the most preferred control systems with their adjustable, adaptable features, flexibility, and robust structure against uncertainty. Control systems developed using FLC are divided into sub-branches of propulsion, energy source, vehicle dynamic system, and environmental effects. In this review study, after revealing what needs to be done about the control system in these branches, it is explained why fuzzy logic is preferred in the solution of these problems and the studies done with FLC are summarized. It has been emphasized that fuzzy logic can be a leading solution tool in cases where the mathematical model cannot be presented. In addition, attention was drawn to optimization algorithms that provide the most accurate parameters to operate a fuzzy system in the most appropriate way, and examples of this were given. [ABSTRACT FROM AUTHOR]

A novel combination of three control systems is presented in this paper: an adaptive control system, a type-two fuzzy logic system, and a super-twisting sliding mode control (STSMC) system. This combination was developed at the Laboratory of Applied Research in Active Controls, Avionics and AeroServoElasticity (LARCASE). This controller incorporates two methods to calculate the gains of the switching term in the STSMC utilizing the particle swarm optimization algorithm: (1) adaptive gains and (2) optimized gains. This methodology was applied to a nonlinear model of the Cessna Citation X business jet aircraft generated by the simulation platform developed at the LARCASE in Simulink/MATLAB (R2022b) for aircraft lateral motion. The platform was validated with flight data obtained from a Level-D research aircraft flight simulator manufactured by the CAE (Montreal, Canada). Level D denotes the highest qualification that the FAA issues for research flight simulators. The performances of controllers were evaluated using the turbulence generated by the Dryden model. The simulation results show that this controller can address both turbulence and existing uncertainties. Finally, the controller was validated for 925 flight conditions over the whole flight envelope for a single configuration using both adaptive and optimized gains in switching terms of the STSMC. [ABSTRACT FROM AUTHOR]

Mould bed is crucial for supporting the ship subsection and controlling its deformation during shipbuilding. However, its visualisation and control process intelligence are often inadequate, leading to reduced accuracy and efficiency in ship subsection construction. Aiming at the problem, a three-dimensional (3D) visual monitoring method for the marine intelligent mould bed is proposed based on digital twin, and the overall framework of the management and control system is designed. Meanwhile, a fuzzy PID control principle optimised by the genetic algorithm is proposed to better control the deformation of the ship subsection. On this basis, the verification of the developed system is carried out based on the physical intelligent mould bed, and the 3D visual management and control of the marine intelligent mould bed are realised. It provides a foundation for improving the accuracy and efficiency of ship subsection construction. [ABSTRACT FROM AUTHOR]

This paper presents the synthesis results of a robust H∞ (H-infinity) loop-shaping controller combined with a PID (Proportional-Integral-Derivative) controller using fuzzy logic for the pitch autopilot of a tail control missile. The results take into consideration the change of mass, moment of inertia, and aerodynamic coefficients. The combination of the control signal of the robust controller and the PID controller is formed on the basis of adjusting the membership function of fuzzy logic according to the ITAE (Integral of Time-multiplied Absolute value of Error) optimization criterion. The performance and robustness of the missile autopilot are verified by nonlinear simulation. [ABSTRACT FROM AUTHOR]

In this technical note, the problem of the extended dissipative control for interval type-2 fuzzy model with nonlinear network systems in the event-triggered framework is investigated. A novel dynamic event-triggered strategy is developed to ascertain the instants of activation. It has the advantage of reducing data transmission and optimising the utilisation of communication resources. On the basis of the proposed mechanism, the stochastic Lyapunov function including internal dynamic variables is constructed. Based on this function, coupled with the inequality scaling technique and the application of Dynkin's formula, we derive a sufficient condition for the system's stochastic asymptotic stability and extended dissipativity. The design of the dynamic event-triggered extended dissipative controller becomes challenging because the new sufficient condition is nonlinear for some matrix variables. However, utilising the Projection Lemma and the co-design approach, such a controller can be developed based on the obtained analysis result. The methodology is demonstrated through a rigorous numerical simulation case study. [ABSTRACT FROM AUTHOR]

This article is concerned with the stability analysis of the static output‐feedback polynomial fuzzy‐model‐based (SOF PFMB) control systems through designing a novel membership grade integration (MGI) approach. The nonconvex problems of the SOF PFMB control systems are convexificated into the convex conditions by introducing block diagonal positive‐definite Lyapunov matrix and nonsingular transformation matrix. We proposed new approximate membership functions, that is, Lagrange membership functions (LMFs), which can be introduced into the stabilization process to relieve the conservativeness of stability results. The LMFs are general representations of piecewise‐linear membership functions, which makes the number of stability conditions not limited by the number of sample points. In a fixed subdomain, arbitrary sample points can be employed by the LMFs method and achieve higher approximation capability by increasing more sample points so that membership grades can be incorporated into the system analysis. Furthermore, a novel MGI approach, including the information of premise variables and LMFs, is proposed making the stability conditions more relaxed. Finally, two simulation examples show the merits of the developed techniques. [ABSTRACT FROM AUTHOR]

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CNC machining realizes the automatic control of machine tools with digital information, which is an advanced technology widely used in today's machinery manufacturing industry. In the process of NC lathe machining of parts, thin‐walled workpieces are prone to deformation due to poor rigidity and thinner wall thickness. Therefore, it is necessary to study the deformation suppression of NC parts. By combining fuzzy control with single neuron PID and introducing fast non dominated sorting genetic algorithm, the parameters of thin‐walled workpiece milling are optimized. The results show that the neuron PID algorithm of fuzzy control has no overshoot in the environment with or without interference, and has fast response speed and strong anti‐interference ability. In the case verification, the cutting force controlled by fuzzy neuron PID can quickly reach the reference 240 N and remain stable. The removal rate fluctuates less in the range of 8000–12,000. It can improve the metal removal rate while maintaining a constant cutting force, so as to restrain the machining deformation of parts. At the same time, the introduced fast non dominated sorting genetic algorithm can increase the maximum rotational speed to 10,486.5, the maximum feed rate per tooth from 0.075 to 0.112, and the rotational speed is nearly doubled, effectively improving the processing quality, providing a technical reference for the stable control of the NC machining system, and providing a new idea and method for part deformation suppression. [ABSTRACT FROM AUTHOR]

Maintaining adequate temperatures for preserving food in a domestic refrigerator is a task that is affected by several factors, including the daily use of the appliance. In this sense, this work presents the development of a novel control system based on fuzzy logic that considers usage habits such as the amount of food entering the refrigerator and the frequency of opening doors. Thus, the control comprises input variables corresponding to the internal temperatures of both compartments, the thermal load entered, and the refrigerator door-opening signal. By simulating the usage habits of a refrigerator with a variable-speed compressor, the control performance was evaluated. The results showed that implementing fuzzy control using usage habits was robust enough to maintain adequate thermal conditions within the compartments and a lower thermal fluctuation concerning the reference control of the refrigerator (factory control). In terms of energy, the fuzzy control resulted in an energy saving of 3.20% with the refrigerator empty (without thermal load) compared to the reference control. On the other hand, the individual integration of the thermal load in the fuzzy control resulted in 2.08% energy savings and 5.45% for the integration of the thermal load compared to the reference control. Finally, considering the combination of usage habits, the fuzzy control presented a higher energy consumption than the reference control, around 9.7%. In this case, the fuzzy control maintained more favorable thermal conditions in both compartments, whereas the reference control presented a warmer thermal condition in the freezer. [ABSTRACT FROM AUTHOR]

Fuzzy-based control systems have demonstrated a remarkable ability to control nonlinear processes, a characteristic commonly observed in power systems, particularly in the context of power quality enhancement. Despite this, an updated and comprehensive literature review on the applications of fuzzy logic in the domain of power quality control has been lacking. To address this gap, this study critically examines published research on the effective and efficient use of fuzzy logic in resolving quality issues within power systems. Data sources included the Web of Science and academic journal databases, followed by an evaluation of target articles based on predefined criteria. The information was then classified into seven categories, including control system type, features of the fuzzy logic controller, fuzzy logic inference strategy, power quality issue, control device, implementation methodology (efficacy testing), and efficiency improvement. Our study revealed that fuzzy-based control systems have evolved from simple type-1 fuzzy controllers to advanced control systems (type-2 fuzzy and hybrid) capable of effectively addressing complex power quality issues. We believe that the insights gained from this study will be useful to both experienced and inexperienced researchers and industry engineers seeking to leverage fuzzy logic to enhance power quality control. [ABSTRACT FROM AUTHOR]

The treatment of low back pain usually includes exercise, analgesics, prostheses, and, in severe cases, surgery. Early treatments based on postural control are essential to prevent low back pain and mitigate permanent damage. This paper presents a wearable device with an estimated cost below $100 that uses inertial units with triaxial accelerometers, gyroscopes, and magnetometers to measure the orientation of three sections of the spine. The device integrates the absolute and relative orientations from the sensors to estimate the posture of the back in real-time and uses a fuzzy system to control a vibration unit that indicates to the user when to correct the posture of the back. This paper validated the device in controlled conditions, obtaining an rms deviation ≤1.24, and conducted a preliminary clinical pilot study with patients afflicted by lumbar hyperlordosis. This paper observed improved postural control and a reduction of low back pain in all cases. These results show the device's promising potential to reduce pain, improve postural therapies, and raise postural awareness in patients with low back pain. [ABSTRACT FROM AUTHOR]

To reduce both the seat's vertical acceleration and cab pitching vibration in vibratory rollers, an active horizontal seat suspension (AHSS) is proposed and researched based on the dynamic model of the vibratory rollers moving and working and off-road grounds. The fuzzy control is applied to calculate the control force of the AHSS. The indexes in the Root Mean Square acceleration in the seat (𝑅𝑀𝑆􀯔􀯦) and cab pitch angle (𝑅𝑀𝑆􀯔􀰝) have been selected as the objective functions. The isolation performance of the HASS is then compared with the cab isolations controlled (CC) and without control (WC) under two conditions of the vibratory roller moving and working on the off-road surface. The study results show that the vibratory roller's quality using the AHSS has been strongly ameliorated in comparison with both the CC and WC under both moving/working conditions. Especially, comparison with the WC, both the values of 𝑅𝑀𝑆􀯔􀯦 and 𝑅𝑀𝑆􀯔􀰝 with the AHSS are greatly reduced by 54.2 % and 52.6 % under the moving case and by 44.5 % and 50.0 % under working conditions of the vibratory roller. Therefore, the AHSS should be applied to improve both the seat's vertical comfort and shaking in vibratory rollers. [ABSTRACT FROM AUTHOR]

In this paper, experimental results about the performance of a Takagi–Sugeno Fuzzy Controller (TSFC) for an EV3 Ballbot Robotic System (EV3BRS) are reported. The physical configuration of the EV3BRS has the form of an inverted pendulum mounted on a ball. The EV3BRS is an underactuated robotic system with four outputs and two control torques. In this work, following the Takagi–Sugeno (TS) fuzzy control design methodology, the Parallel Distributed Compensation (PDC) approach is used in the design of the TSFC. The EV3BRS's TS Fuzzy Model (TSFM) design comes from linearization of the nonlinear model around two operation points near the upright position of EV3BRS's body. The Linear Matrix Inequality (LMI) approach was used to obtain the feedback gains for every local linear controller, guaranteeing, via a conservative stability condition, the global asymptotic stability of the overall fuzzy control system. The main goal of the control task consists of maintaining the EV3BRS's body at its upright position. Measurement and control data from and to the EV3BRS are transferred via telecontrol and telemetry. The appropriate performance of the controller design is corroborated via experimentation. [ABSTRACT FROM AUTHOR]

Many dynamic processes are characterized by parametric or structural uncertainties due to internal and external disturbances. Existing deterministic models could not handle the uncertainties inherent in these processes. A valuable alternative to control these processes is the use of a type-3 fuzzy system. Since type-3 fuzzy systems use threedimensional membership functions, they have more capacity to model uncertainties. This paper introduces the design of a type-3 fuzzy logic system (FLS) for the control of dynamic plants. Utilizing type-3 fuzzy logic, the architecture of the type-3 fuzzy control system (T3FCS) is proposed. The knowledge base of the controller is constructed and its design stages are presented. The inference mechanism of type-3 FLS is developed using α slices and interval type-3 membership functions. The proposed type-3 FLS is utilized for controlling nonlinear dynamic plants. The modeling of the proposed T3FCS is performed and transient response characteristic is derived using different stepwise excitation signals. A comparison of the designed system with the type-1 FLS-based system is provided. The obtained simulation result demonstrates the efficiency of using the proposed type-3 FLS in the control of dynamic systems characterized by uncertainties. [ABSTRACT FROM AUTHOR]

Summary: This article investigates the devise issue of a practical finite‐time output‐tracking control for uncertain nonlinear nonstrict‐feedback systems subject simultaneously to time‐varying output constraints (TVOC), actuator saturation, and bounded unmatched disturbances. An adaptive fuzzy approximator‐based control system is devised using the dynamic surface control (DSC) concept. System output constraints are addressed by employing an appropriate barrier Lyapunov function. Adaptive fuzzy disturbance observers are constructed to practically deal with time‐varying unmatched but bounded disturbances. It is verified via Lyapunov stability analysis and simulation experiments that all closed‐loop signals remain bounded, the output tracking error practically converges in finite time to a tiny but adjustable neighborhood around the origin, and the system output stays within its required time‐varying bounds. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of exponential H ∞ output control for switching fuzzy systems, considering both impulse and non-impulse scenarios. Unlike previous research, where the average dwell time (ADT: τ a ) and the upper bound of inter-event intervals (IEIs: T) satisfy the condition τ a ≥ ln μ + (α + β) T α = ln μ + β T α + T , implying that frequent switching is difficult to achieve, this paper demonstrates that by adopting the mode-dependent event-triggered mechanism (ETM) and a switching law, frequent switching is indeed achieved. Moreover, the question of deriving the normal L 2 norm constraint is solved through the ADT method, although only a weighted L 2 norm constraint was obtained previously. Additionally, by constructing a controller-mode-dependent Lyapunov function and adopting logarithmic quantizers, the sufficient criteria of exponential H ∞ output control problem are presented. The validity of established results is demonstrated by a given numerical simulation. [ABSTRACT FROM AUTHOR]

Fire incidences are classed as catastrophic events, which mean that persons may experience mental distress and trauma. The development of a robotic vehicle specifically designed for fire extinguishing purposes has significant implications, as it not only addresses the issue of fire but also aims to safeguard human lives and minimize the extent of damage caused by indoor fire occurrences. The primary goal of the AFRC is to undergo a metamorphosis, allowing it to operate autonomously as a specialized support vehicle designed exclusively for the task of identifying and extinguishing fires. Researchers have undertaken the tasks of constructing an autonomous vehicle with robotic capabilities, devising a universal algorithm to be employed in the robotic firefighting process, and designing a fuzzy controller algorithm that can be used in all expected scenarios. The use of a fuzzy logic algorithm in this design demonstrates the usefulness of this system, all factors are involved in which cases are previously identified and taught, as well as the overall map of the premises have been uploaded so that the system can identify the exact place of the fire source, and two types of fire have also been examined. When the performance of the foam pump, water pump, and robotic car motors is compared to the data from the flam sensor, temperature sensor and GPS data, it demonstrates a high responsiveness in terms of applying the appropriate approach based on the type of fire due to the probable action for which the system has been trained. This will have the benefit of shortening the required process for fire extinguishment and using the appropriate fire extinguishing tools. This technology may be used to put out flames, deploy in different areas, and handle a variety of fire scenarios inside buildings. [ABSTRACT FROM AUTHOR]

Rechargeable batteries are the most widely used medium for storing energy today. One type of rechargeable battery that is widely used is lithium-ion batteries. The large use of lithium-ion batteries in society requires companies to research so that the lifetime of these batteries can last a long time and charging can take place quickly. The charging system at this time is less efficient in charging lithium batteries where the time needed is still quite long whereas when lithium batteries are charged with a long time can cause the battery to heat up quickly and can reduce the lifetime of the battery. To overcome this, a system is needed that can control the battery charger process so that the output voltage and current are constant and battery charging is faster. It is hoped that the SEPIC converter system can help many people who forget to unplug the power supply during the charging process to maintain the lifetime of the battery. Setting the output voltage and current in the DC-DC converter can be done using an Adaptive Neuro Fuzzy Inference System which aims to keep the output of SEPIC stable according to the setting point. In this system, the DC-DC converter used is a SEPIC converter which can increase and decrease the output voltage for battery charging. The battery charging process uses the CC-CV method. In the test, the average error is 0.025% whereas when the SOC is 60% to 80% the average error is 0.04%, and when the SOC is 80% to 95% the average error is 0.0005%. [ABSTRACT FROM AUTHOR]

Parallel robot systems are increasingly important and widely applied due to their superior advantages such as high speed and accuracy. To improve the accuracy of these systems, recent research has focused on developing advanced control methods. However, this remains a significant challenge due to the complex mathematical model of parallel robots. This study introduces a control system based on a fuzzy cerebellar model articulation controller (FCMAC) to control parallel robots. The proposed control system includes FCMAC as the main tracking controller used to estimate the ideal control. A robust controller is employed to compensate for the error between FCMAC and the ideal controller. The parameters of FCMAC are adjusted online based on adaptive laws derived from Lyapunov functions. Finally, a five-bar parallel robot is selected to experiment with the FCMAC algorithm to demonstrate the effectiveness of the proposed controller. The results show that the accuracy of FCMAC is better than that of other algorithms. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles have a wide range of uses in the military field, non-combat situations, and civil works. Due to their ease of operation, unmanned aerial vehicles (UAVs) are highly sought after by farmers and are considered the best agricultural technologies, since different types of controller algorithms are being integrated into drone systems, making drones the most affordable option for smart agriculture sectors. PID controllers are among the controllers frequently incorporated into drone systems. Although PID controllers are frequently used in drones, they have some limitations, such as sensitivity to noise and measurement errors, which can lead to instability or oscillations in the system. On the other hand, PID controllers provide improved accuracy in drone system responses. When using PID controllers to achieve the best performance in a drone system, it is better to share the advantages of PID controllers with other intelligence controllers. One promising option is the fuzzy PID controller. The aim of this study was to control quadcopter states (rolling, altitude, and airspeed) by leveraging quadcopter technology and adding hybrid fuzzy PID controls into the system. The quadcopter system and its controllers were mathematically modeled using the Simulink/MATLAB platform, and the system was controlled by fuzzy PID controllers. For validation purposes, the fuzzy PID controller was compared with a classically tuned PID controller. For roll, height, and airspeed, the fuzzy PID controller provided an improvement of 41.5%, 11%, and 44%, respectively, over the classically tuned PID controller. Therefore, the fuzzy PID controller best suits the needs of farmers and is compatible with smart agriculture systems. [ABSTRACT FROM AUTHOR]

This paper introduces an analog integrated fractional order type-2 fuzzy PID control system. Current approaches frequently depend on energy-intensive embedded digital systems, consuming substantial energy levels ranging from a few μW to mW. To address this limitation we propose a fully analog design offering insights into the potential of analog circuits for powerefficient robust control in complex and uncertain environments. It consists of Gaussian function, min/max, Operational transcoductance amplifier circuits and Resistor-Capacitor networks for the implementation of the fractional-order components. Crafted for operation under a reduced voltage supply (0.6 V), the controller attains minimal power usage (861.8 nW), facilitating uninterrupted, extended-term functioning. Post-layout simulation results confirm the proper operation of the proposed design. The proposed system is designed and simulated using the Cadence IC Suite in a TSMC 90 nm CMOS process. [ABSTRACT FROM AUTHOR]

In this paper, the security control problem for T-S fuzzy systems is investigated through the sliding mode control (SMC) strategy. Consider deception attacks occurring randomly, a multi-modes round-robin protocol is proposed on the sensor-to-controller channel. Under this protocol, the number of transmitted nodes can be dynamically adjusted according to attack probability and system state so as to balance the relationship between system performance and communication transmission. The fuzzy sliding mode controller depending on the scheduling signal is constructed, whose membership functions (MFs) are different from the ones of the controlled system. In addition, by using the membership-function-dependent (MFD) stability analysis method, appropriate sufficient conditions are derived to ensure both the stochastic stability and the reachability. Finally, simulation results are presented to prove the proposed SMC strategy. [ABSTRACT FROM AUTHOR]

The purpose of this work is to investigate the construction of fuzzy embedded control systems combining fast execution and parallel processing capabilities provided by Field Programmable Gate Arrays (FPGAs) and Reconfigurable Inputs/Outputs (RIO) chips. A fixed-point fuzzy controller is developed and implemented on a fast mechatronic system with high-speed control and high channel count on an FPGA target. This paper provides a brief introduction to deploying Fuzzy Logic Control (FLC) methods using RIO-FPGA technology. It suggests a technique for implementing the three stages that constitute the FLC and the PD-like FLC and PID-like FLC structures into practice. Controllers with 1 and 2 degrees of freedom are developed and tested experimentally. Parallel loops, key challenging advantage of LabVIEW programming, are utilized for decoding feedback signals, generating pulse trains for actuator's drivers and for calculation of control gains. An NI-SbRIO board that combines deployable devices with a real-time processor, a re-configurable FPGA, and analogue and digital input–output ports is used. The experimental work demonstrates the significant enhancement of implementing reconfigurable embedded fuzzy control upon such mechatronic systems. [ABSTRACT FROM AUTHOR]

Project success and achieving project objectives and goals highly depend on effective and thorough risk management implementation. This study provides a comprehensive and practical methodology for project risk management. In this paper, firstly, the risks were collected by analyzing the historical documents and literature. Then, the collected risks were screened using brainstorming and categorized into five groups. Subsequently, a questionnaire was made and the identified risks were validated using the Fuzzy Delphi technique. Also, the relationships between risks were determined using the Interpretive Structural Modelling (ISM) method. Moreover, the weights of the criteria used to rank the risks were calculated through the Fuzzy Best-Worst Method. Subsequently, the major risks were determined using the fuzzy WASPAS method. Furthermore, a novel biobjective mathematical programming model was developed and solved using the Augmented Epsilon-Constraint (AEC) method to choose the optimal risk response strategies for each critical risk. The results demonstrated that the proposed framework is effective in dealing with construction project risks. [ABSTRACT FROM AUTHOR]

This article discusses the challenges of scaling up photocatalytic advanced oxidation processes (AOPs) for the treatment of wastewater. While AOPs have shown promise in laboratory settings, their large-scale implementation has been limited due to the lack of control and automation strategies. The article explores the use of intelligent control systems, such as neural networks and fuzzy logic control, to optimize AOPs. The authors propose a fuzzy control system that uses pH as a control variable to regulate the doses of reagents in the AOP. The study demonstrates the effectiveness of the control algorithm in achieving maximum nutrient content in the treated water. The article emphasizes the importance of acquiring deep knowledge about the plant and using Programmable Logic Controllers for scaling AOPs to industrial environments. [Extracted from the article]

Recently, quantum computing has been proven as an ideal theory for the design of fuzzy inference engines, thanks to its capability to efficiently solve the rule explosion problem. In this scenario, a quantum fuzzy inference engine (QFIE) was proposed as a quantum algorithm able to generate an exponential computational advantage over conventional fuzzy inference engines. However, there are no practical demonstrations that the QFIE can be used to efficiently manage complex systems. This article bridges this gap by using, for the very first time, the QFIE to control critical systems such as those related to particle accelerator facilities at the European Organization for Nuclear Research (CERN). As demonstrated by a series of experiments performed at the T4 target station of the CERN Super Proton Synchrotron fixed-target physics beamline and at the Advanced Proton Driven Plasma Wakefield Acceleration Experiment, the QFIE is able to efficiently control such an environment, paving the way for the use of fuzzy-enabled quantum computers in real-world applications.

A pulse-Doppler (PD) radar has the advantage of strong anti-interference ability, and it is often used as a solution for maneuvering target tracking. In the application of target monitoring and tracking in PD radars, the interacting multiple model algorithm (IMM) has become the main and preferred choice due to its flexibility and high accuracy. However, the probability transfer matrix in classical IMM algorithms generally depends on constant prior knowledge, and if a PD radar is tracking a strong maneuvering target, it is inevitable to encounter some limitations, such as the possibility of target tracking trajectory deviation, and even a loss of the target. The Markov probability transfer matrix is proposed with an adaptive modification ability in real time to overcome the above problems in this paper. Additionally, for improving the speed of switching between the models, the fuzzy control system for secondary updating of model probability is adopted. By this means, the tracking accuracy of maneuvering targets is enhanced. Compared with the classical IMM algorithm, the corresponding simulation results for the PD radar indicate that the overall tracking accuracy of the proposed adaptive IMM algorithm is improved by 19.6%. In conclusion, the continuity and accuracy of the target trajectory can be effectively improved with the proposed adaptive IMM algorithm in PD radar cases. [ABSTRACT FROM AUTHOR]

In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the coupling characteristics of multiple systems, and investigates the energy management and optimal control mechanisms. Firstly, a power transmission scheme is designed for a hybrid electric vehicle. Then, a multi-state model is established to assess the electric vehicle's performance under complex working conditions and explore how these conditions impact system coupling. Next, load power is redistributed using the Haar wavelet theory. The super capacitor is employed to stabilize chaotic and transient components in the required power, with low-frequency components serving as input variables for the controller. Further, power distribution is determined through the application of fuzzy logic theory. Input parameters include the system's power requirements, power battery status, and super capacitor state of charge. The result is the output of a composite power supply distribution factor. To fully exploit the composite power supply's potential and optimize the overall system performance, a global optimization control strategy using the dynamic programming algorithm is explored. The optimization objective is to minimize power loss within the composite power system, and the optimal control is calculated through interpolation using the interp function. Finally, a comparative simulation experiment is conducted under UDDS cycle conditions. The results show that the composite power system improved the battery discharge efficiency and reduced the number of discharge cycles and discharge current of the power battery. Under the cyclic working condition of 1369 s, the state of charge of the power battery in the hybrid power system decreases from 0.9 to 0.69, representing a 12.5% increase compared to the single power system. The peak current of the power battery in the hybrid power system decreases by approximately 20 A compared with that in the single power system. Based on dynamic programming optimization, the state of charge of the power battery decreases from 0.9 to 0.724. Compared with that of the single power system, the power consumption of the proposed system increases by 25%, that of the hybrid power fuzzy control system increases by 14.2%, and that of the vehicle decreases by 14.7% after dynamic programming optimization. The multimode energy shunt relationship is solved through efficient and reasonable energy management and optimization strategies. The performance and advantages of the composite energy storage system are fully utilized. This approach provides a new idea for the energy storage scheme of new energy vehicles. [ABSTRACT FROM AUTHOR]

Expanding upon the well-established Takagi–Sugeno (T–S) fuzzy model, the T–S fuzzy descriptor model emerges as a robust and flexible framework. This article introduces the development of optimal and robust-optimal controllers grounded in the principles of stability control and fuzzy descriptor systems. By transforming complicated inequalities into linear matrix inequalities (LMI), we establish the essential conditions for controller construction, as delineated in theorems. To substantiate the utility of these controllers, we employ the rotary inverted pendulum as a testbed. Through diverse simulation scenarios, these controllers, rooted in fuzzy descriptor systems, demonstrate their practicality and effectiveness in ensuring the stable control of inverted pendulum systems, even in the presence of uncertainties within the model. This study highlights the adaptability and robustness of fuzzy descriptor-based controllers, paving the way for advanced control strategies in complex and uncertain environments. [ABSTRACT FROM AUTHOR]

As significant carriers of the application of fuzzy set theories, fuzzy systems have been widely used in many fields. However, selecting fuzzifications, fuzzy reasoning engines, and defuzzifications is subjective for Mamdani fuzzy systems, and the fuzzy rule of Takagi-Sugeno-Kang fuzzy systems is less of a linguistic interpretation. Regarding these shortcomings, this paper proposes a fuzzy system based on vague partitions processing information directly from the fuzzy rule base, in which fuzzy rules have explicit semantics. Firstly, the n-dimensional vague partition of the n-dimensional universe is defined based on 1-dimensional vague partitions and the aggregation function, and its properties are discussed. Based on these, we design the new fuzzy system, and investigate its approximation properties which is the theoretical guarantee for applying the fuzzy system. As an application, we combine the fuzzy system with PID control system to deal with autonomous vehicle path tracking control problems. A series of experiments are constructed, and experimental results indicate that the fuzzy system based on vague partitions makes the fuzzy PID control system strong robustness, and has obvious advantages compared with other traditional fuzzy systems for path tracking control problems. [ABSTRACT FROM AUTHOR]

This paper addresses the finite-time control problem of a class of uncertain nonlinear systems subject to input saturation and actuator faults. To approximate the unknown system states, a fuzzy state observer is established where fuzzy logic systems are utilized to estimate the unknown nonlinearities. A nonlinear disturbance observer to estimate the external disturbance of the system. A new finite-time adaptive fuzzy controller is constructed together with the proposed disturbance and state observers, which can guarantee the tracking error into a small neighborhood around zero within finite time. To avoid the tedious and arithmetic problems brought by the traditional backstepping control, the dynamic surface technique is applied, which can effectively reduce the computation burden. It can be proved that not only the boundedness of the closed-loop system states can be guaranteed but also the tracking error is regulated into a small range near the equilibrium in finite time, despite unknown actuator faults and input saturation. Finally, the simulations of two-stage chemical reactor nonlinear system are conducted to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The paper studies the problem of robust tracking of the fuzzy systems in the presence of uncertainties. A robust feedback controller based on the linear matrix inequality (LMI) is proposed using a Lyapunov function combined with the supervisory controller. Hence, the sufficient stability condition for the general type of robust fuzzy tracking controls has been formulated and the performance of the controller has been tested for different classes of the fuzzy models. It has been demonstrated that the smooth fuzzy compositions have better performance rather than the standard fuzzy compositions, (i.e., min–max compositions and product–sum compositions). Considering that the smooth fuzzy compositions are efficient in face of the uncertainties, it is believed that the present work opens up new doors for wider applications of the smooth fuzzy compositions in the industry. [ABSTRACT FROM AUTHOR]

The asymptotic behavior of solutions of fuzzy control systems is a component of the study of fuzzy control theory. The study of stability for T-S (Takagi-Sugeno) fuzzy systems, which process qualitative data through linguistic expressions, is the subject of this paper. Asymptotic stability is conservative in many real-world applications due to measurement noise and other disruptions. The ultimate limit, which indicates that the mistakes stay in a specific area close to the origin after a long enough amount of time, is a crucial characteristic that is frequently defined for such systems. We are interested with the problem of the state feedback controller for T-S fuzzy models with uncertainties where the global exponential ultimate boundedness of solutions is studied for certain fuzzy control systems. We use common quadratic Lyapunov function and parallel distributed compensation controller techniques to study the asymptotic behavior of the solutions of fuzzy control system in presence of perturbations. An example demonstrating the validity of the main result is discussed. [ABSTRACT FROM AUTHOR]

Summary: A Robust H2$$ {H}_2 $$ Nonlinear Fuzzy Decentralized Controller (H2$$ {H}_2 $$ RNFDC) is proposed to improve the overall robustness and tracking ability of a VSC‐HVDC link. The studied system is considered as composed by two overlapping nonlinear subsystems. The nonlinear interaction between the two subsystems is treated as a disturbance rejection problem by fuzzy control techniques. First, the Takagi Sugeno (TS) fuzzy model is adopted for fuzzy modeling of the uncertain nonlinear system. Next, new stability conditions for a generalized class of uncertain HVDC systems are derived from robust control techniques based on Linear Matrix Inequalities (LMIs). The design method employs the so‐called Parallel Distributed Compensation (PDC) to obtain H2$$ {H}_2 $$ RNFDC gains based on LMIs.The efficiency and robustness of the proposed controllers are analytically proven and tested through validation simulations. The main contributions of this paper are: (i) resilience: in case of failure of one converter or loss of measures or controls, the control of the other converter is not affected; (ii) robustness is improved in order to provide good responses in case of network variations and HVDC line parameters changes; (iii) fuzzy techniques are adopted to handle nonlinearities and changes of operating conditions. The proposed H2$$ {H}_2 $$ RNFDC is compared with Fuzzy H 2$$ {}_2 $$ Decentralized State Feedback Control (H2$$ {H}_2 $$ NFDC) and H∞$$ {H}_{\infty } $$ Linear Decentralized Control (H∞$$ {H}_{\infty } $$ LDC) in simulation to illustrate the control synthesis and its effectiveness. [ABSTRACT FROM AUTHOR]

Nowadays, controlling industrial processes and choosing the proper method for this purpose is very important. DC–DC converters are widely used in industrial applications, such as DC–DC step‐up converters of 150 and 250 V in power drives and power supplies of portable electrical and hybrid vehicles. A predictive control algorithm is a method to deal with such complex processes. Many methods have been proposed as predictor control, leading to better and more accurate evolution with problems. This study proposes a fuzzy explicit predictive control method for adjusting the boost DC–DC converter. A fuzzy method for selecting the weights of the cost function of the predictive control algorithm of the DC–DC converter will be presented. In addition, for better evaluation and analysis, the designed controller is compared with similar methods, and the simulation results show that the controller designed on this system has had a proper performance with other methods in realizing the desired goals. [ABSTRACT FROM AUTHOR]