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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]

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]

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 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]

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 principle of subordination is useful in comparing two holomorphic functions when the range of one holomorphic function is a subset of the other and they comply at a single point. The subordination, when spoken in fuzzy set theory, becomes fuzzy subordination as the comparison between two holomorphic functions is made using the fuzzy membership function. In this article, differential and fuzzy differential Subordination, superordination, and sandwich theorems have been discussed for the classes defined by using q-derivative and symmetric q-derivative operators. [ABSTRACT FROM AUTHOR]

Within the framework of the step‐function method, the stability of a nonlinear fuzzy hybrid control system that incorporates both an impulsive controller and a continuous state feedback controller is investigated. Both the two controllers are assumed to be subject to both actuator saturation and time‐varying delays, which has been given scant attention, if any, in the existing studies. A new assumption is introduced in this article, thereby enabling the use of generalized sector conditions to tackle the double saturation, and the conservatism inherent in stability results is remarkably reduced owing to the improved step‐function approach employed. The stability theorem proposed in this article removes constraints imposed on the time delays of both controllers, which can be also applied to wider scopes of systems, encompassing hybrid control systems with both stabilizing and instabilizing impulses, systems with varying impulsive gain, and systems with Zeno behavior. Numerical simulations of stabilization for different systems by delayed saturated hybrid control have been conducted, which demonstrate the validity of the proposed theorems. [ABSTRACT FROM AUTHOR]

This article is concerned with the adaptive event‐triggered (AET) efficient output feedback model predictive control (EOFMPC) for a set of nonlinear networked interval type‐2 Takagi–Sugeno (IT2 T–S) fuzzy control systems with data loss. For the reduction in data transmission burden and required control performance, the AET scheme and a novel dynamic triggering law are introduced jointly in this article. The efficient model predictive control approach as in Kouvaritakis et al. (IEEE Trans Automat Contr. 2000;45(8):1545–1549.), which transfers the main part of online optimization work to offline stage by incorporating an extra perturbation into the fixed feedback law, is extended to networked control systems to reduce their online computational burden. This article proposes the following: (1) an unified closed‐loop model representation of data loss process and IT2 T–S fuzzy nonlinearities is established under the framework of EOFMPC, which explicitly considers the AET condition; (2) a new design method for the state observer gain is presented based on an ellipsoidal invariant set, so that a smaller bound of estimation error can be obtained through an extra optimization problem; (3) some optimization problems are formulated to design the proposed controllers, and the corresponding EOFMPC algorithm is summarized; (4) by means of the extra matrix partition technique, the online optimization problem subject to one ellipsoidal constraint is transformed into a novel convex form to solve the uncertainty of the estimation error. A simulation example and a few comparative simulation experiments illustrate the effectiveness and availability of the aforementioned approach. [ABSTRACT FROM AUTHOR]

This paper introduces a fuzzy logic-based autonomous ship deck landing system for fixed-wing unmanned aerial vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing UAVs on moving ships in challenging maritime conditions, relieving operators from this demanding task. The designed UAV ship deck landing model is based on a fuzzy logic system (FLS), which comprises three interconnected subsystems (speed, lateral motion, and altitude components). Each subsystem consists of three inputs and one output incorporating various fuzzy rules to account for external factors during ship deck landings. Specifically, the FLS receives five inputs: the range from the deck, the relative wind direction and speed, the airspeed, and the UAV's flight altitude. The FLS outputs provide data on the speed of the UAV relative to the ship's velocity, the bank angle (BA), and the angle of descent (AOD) of the UAV. The performance of the designed intelligent ship deck landing system was evaluated using the standard configuration of MATLAB Fuzzy Toolbox. [ABSTRACT FROM AUTHOR]

This study delves into the development and analysis of a novel Embedded Fuzzy Type 2 PID Controller for Robot Manipulators, motivated by the increasing need for enhanced control systems in robotic applications to improve precision and stability. In the background section, the limitations of conventional PID controllers in addressing uncertainties and disturbances, especially in complex tasks performed by robot manipulators, are presented. The concept of fuzzy logic and the Type 2 fuzzy system is introduced, highlighting their potential to manage imprecise and uncertain information. Through rigorous analysis and simulation, the superior performance of the Embedded Fuzzy Type 2 PID Controller is demonstrated when compared to traditional PID controllers and even Type 1 fuzzy controllers. The results showcase enhanced tracking accuracy, disturbance rejection, and adaptability, making it a promising solution for advanced robotic applications. In conclusion, this research provides a robust solution for improving the control of robot manipulators in uncertain and dynamic environments. The Embedded Fuzzy Type 2 PID Controller offers a new paradigm in control theory, ensuring stability and precision even in the face of unpredictable factors. This innovation holds great promise for advancing the capabilities of robotic systems and underlines the potential for further research in embedded fuzzy control systems. [ABSTRACT FROM AUTHOR]

Purpose: Considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator and preview controller, is considered to address the tracking control problem. Design/methodology/approach: The authors employ an augmentation technique to construct an augmented error system for uncertain T-S fuzzy discrete-time systems with time-varying uncertainties. Additionally, the authors obtain the corresponding linear matrix inequality (LMI) conditions for designing the preview controller. Findings: This paper discusses the preview tracking problem for nonlinear systems. First, considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator, and preview controller, is considered to address the tracking control problem. Then, using the fuzzy Lyapunov functional with the linear matrix inequality (LMI) technique, new sufficient conditions for the asymptotic stability of the augmented system are derived by applying the LMI technique. The preview controller and fuzzy observer can be designed in one step. Finally, a numerical example is used to illustrate the effectiveness of the results. Originality/value: An augmented error system is successfully constructed by the state augmentation approach. A novel preview controller is designed to address the tracking control problem. The preview controller and fuzzy observer can be designed in one step. [ABSTRACT FROM AUTHOR]

The paper discusses the threat of decommissioning to the thermal power plant (TPP) heat exchanger tubes because of erosion and develops a computer-integrated control system (CICS) for the process of distribution of steam coal flows with different indicators of abrasive materials content, which is based on fuzzy logic. The problem of rapid decommissioning of TPP heat exchangers, particularly abrasive damage to furnace screen tubes, economizer, superheater, etc. This may indicate a discrepancy between the expected fuel ash content and the actual one, as well as a high content of abrasive impurities in steam coal. The work aims to develop a CICS of the wear resistance of the heat exchange surface of a steam boiler of a coal-fired power plant by measuring and fuzzy control of the content of abrasive impurities in steam coal. The problems of damage to the equipment of the TPP boiler are investigated, and a system for controlling the wear resistance of the surface by automatic fuzzy control of the quality of coal is developed. The results were investigated during coal preparation and combustion in the furnace of a thermal power plant to investigate the effectiveness of the proposed fuzzy controller. The model results confirm the feasibility of the fuzzy control method for the system with different coal quality parameters. [ABSTRACT FROM AUTHOR]

In this study, the two-wheeled mobile robot (TWMR) was controlled by taking the user's mass and center of gravity as a reference instead of the joystick control of the TWMR. A load-cell-based original TWMR system was proposed and realized. The behavior of the driving dynamics was discussed with a developed method different from other studies. The mathematical model of a load cell-based TWMR system was created and then simulated in the Matlab Simulink environment. This feature allowed the system to be more controlled and stable. The designed TWMR systems were controlled with proportional, integral, and derivative (PID) and fuzzy-PID controllers in the simulation. PID parameters in the system vary according to the mass of the user. The change of PID parameters was provided according to the mass information obtained from the load cell with the fuzzy-PID controller. Thus, the ability of the system to stay in balance was increased. Additionally, the speed of the TWMR and the pitching angle were controlled using PID and fuzzy-PID methods. The current values drawn from the battery were determined according to the applied controllers on 0° and 3° slope roads. The speed control of the motors was carried out with a mean error of 2%. The results of the fuzzy-PID control were observed to be better than those of the traditional PID controller. [ABSTRACT FROM AUTHOR]

The extraction of essential oils in the world is widely used to combat various diseases, and also helps for giving a good taste to food (flavoring condiments) and cosmetics that do not pollute the environment or generate chemicals. Peru is in the top 10 countries with the greatest biological diversity in the world, having an approximate 10% of world flora and endless endemic species, also valuing the citrus fruits having productive of orange, lime, grapefruit, tangerine, and tangelo. Therefore, processes are analyzed and a model for the extraction of essential oil from citrus fruits is developed, through the design of an industrial-scale steam extractor, which establishes the automation of the filling and emptying of the distiller to obtain better results and also automates a closed-loop control for refrigeration, to help the operator to control and supervise the process of refrigeration and water filling using a control panel. The distillation process was analyzed where an average of 1% to 5% of essential oil of citrus fruits was obtained and the development of a control for the supervision of the filling and emptying of the water was analyzed. In this paper we introduce a Neutrosophic (Indeterminate) Control System model based on the well-known Fuzzy Control Systems models, especially Mamdani's. It is applied in the process of automating the extraction of essential oil from citrus fruits. An Indeterminate Control System, like its similar Fuzzy Control System, makes it possible to control the oil production process with the help of natural language. The advantage of the Indeterminate Control System is that it explicitly considers indeterminacy due to the non-homogeneity of the parameters within the system, thus it is more accurate. [ABSTRACT FROM AUTHOR]

Compared with type-2 fuzzy sets, the secondary membership degree of interval type-3 fuzzy sets is an interval rather than crisp value, which makes interval type-3 fuzzy sets can obtain more degree of freedoms. This article studies an interval type-3 fuzzy PID controller based on interval type-3 fuzz sets. The framework of interval type-3 fuzzy PID controller is identical with type-2 fuzzy PID controller, but it contains more adjustment controller parameters and its type reduction procedure is more complex. In this paper, type reduction of interval type-3 fuzzy sets is derived from general type-2 fuzzy sets represented by α-plane and a direct NT type reduction algorithm is applied. The control effects of interval type-3 fuzzy PID controller are firstly tested by 2 nonlinear plants, the simulation results show that interval type-3 fuzzy PID controller has better control performance indexes than PID controller, type-1 fuzzy PID controller, interval type-2 fuzzy PID controller and general type-2 fuzzy PID controller. Furthermore, the interval type-3 fuzzy PID controller will be applied in rated voltage control of solid oxide fuel cells (SOFC) power plant. The output voltage control of SOFC is quite challenging because of the strong nonlinearity, limited fuel flow, and rapid variation of the load disturbance. The simulation results demonstrate the advantages and robustness of proposed interval type-3 fuzzy PID controller. [ABSTRACT FROM AUTHOR]

In this study, the security quantized control problem is investigated for discrete‐time Takagi‐Sugeno (T‐S) fuzzy systems with deception attacks based on the multi‐channel‐enabled round‐robin (MCERR) protocol. The MCERR protocol breaks through the limitation in the well‐known RR protocol where only one signal can be transmitted at every turn. Under MCERR protocol, multiple transmission channels are put to use and a set of sensor nodes can gain access to network. Considering the random deception attacks and the measurement outliers, a new system model is established and a sufficient condition is derived to ensure the stability of the discussed system. Furthermore, an outlier resistant observer‐based controller which can mitigate the impact of deception attacks is developed. Besides, the concrete expressions of observer gains and controller gains are obtained. Finally, the effectiveness of the proposed method can be validated by simulation results. [ABSTRACT FROM AUTHOR]

Routing protocols based on trust mechanisms have been widely investigated for wireless sensor networks, and the works have achieved good results, while there are few works on trusted routing for underwater acoustic networks (UANs). However, trust-aware routing is the key to improving the packet delivery rate and the energy efficiency of UANs. Therefore, inspired by the theory of trust evaluation, a trust-aware and fuzzy logic-based reliable layering routing protocol (TAFLRLR) is proposed. In the TAFLRLR protocol, to avoid the problem of the void area and improve the transmission reliability, the candidate nodes of the next-hop forwarding nodes are determined according to the layers of neighbor nodes. Moreover, a fuzzy logic-based trust evaluation mechanism (FLTEM) is provided, which employs the fuzzy comprehensive evaluation decision model to calculate the comprehensive trust value for underwater sensor nodes. Further, the node density of a candidate node and its comprehensive trust value are taken as the input of a fuzzy control system and the forwarding probability (FP) of the node is taken as the output, and the candidate node with the highest FP is selected as the best forwarding node. Simulation results illustrate the superiority and effectiveness of the TAFLRLR protocol in terms of energy efficiency, routing reliability, and transmission reliability. [ABSTRACT FROM AUTHOR]

At present, the switching control methods based on time derivative of membership functions are not suitable for the T‐S fuzzy systems with non‐differentiable membership functions. In this paper, the switching control problem of T‐S fuzzy systems subject to mixed delays with non‐differentiable membership functions is studied. We use the difference between the real‐time and the stable membership value of the T‐S system as the basis for switching, and design two kinds of switching conditions. One is to combine the difference with the system matrices to adjust the description form of the system model. The other is to adjust the controller form by combining the difference value with the control gains. The proposed methods are also applicable to the case of the differentiable membership function. Compared with the switching condition using the derivative, the two proposed switching conditions both contain fewer unknown parameters and reduce the computational complexity. Three examples are given to illustrate the advancement and validity of the switching conditions. [ABSTRACT FROM AUTHOR]

In order to improve the coordinated control effect of hierarchical power balance of new energy microgrid, this paper applies fuzzy control method to this system, and proposes a hierarchical control strategy based on event-triggered communication. Each DG is regarded as a proxy, and the continuous actual value of output is replaced by the state prediction value. Moreover, two different event trigger condition functions for frequency and voltage are designed based on Lyapunov method respectively. At the same time, each DG only communicates with its neighbor DG aperiodic at the event trigger time, and finally all DG are restored to the reference value provided by the virtual leader. Finally, this paper constructs a coordinated fuzzy control simulation system for hierarchical power balance of new energy microgrid. Combined with the simulation results, the method proposed in this paper is feasible. [ABSTRACT FROM AUTHOR]

This study presents a novel observer-based fuzzy control for chaotic systems (CSs). A class of CSs with unmeasurable states, unknown input constraints and unknown dynamics is taken to account. A generalised type-2 (GT2) fuzzy logic system (FLS) is formulated to approximate the uncertainties. The parameters of GT2-FLS are adjusted through the tuning rules that are extracted from robustness investigation. The proposed method is applied on CSs and it is demonstrated that the suggested observer and control system result in good performance in versus of disturbances, uncertainties, unknown and time-varying input nonlinearities, unmeasurable states and noisy faults. Comparison with other type of fuzzy systems demonstrates that the proposed GT2-FLS based controller is more effective. [ABSTRACT FROM AUTHOR]

Following the European Climate Law of 2021 and the climate neutrality goal for zero-emission transportation by 2050, electric vehicles continue to gain market share, reaching 2.5 million vehicles in Q1 of 2023. Electric vehicles utilize an electric motor for propulsion powered by lithium batteries, which suffer from high temperatures caused by peak operation conditions and rapid charging, so hybridization with supercapacitors is implemented. In this paper, a fuzzy logic controller is employed based on a rule-based scheme and the Mamdani model to control the power distribution of the hybrid system, driven by the state of charge and duty cycle parameters. An active topology with one bi-directional DC-to-DC converter at each source is exploited in the MATLAB/Simulink environment, and five power states like acceleration and coasting are identified. Results show that the ideal duty cycle is within 0.40–0.50 as a universal value for all power states, which may vary depending on the available state of charge. Total efficiency is enhanced by 6%, sizing is increased by 22%, leading to a more compact layout, and battery life is extended by 20%. Future work includes testing with larger energy sources and the application of this management strategy in real-time operations. [ABSTRACT FROM AUTHOR]

From the point of view of the synthesis of control influences, arc steelmaking furnaces are complex nonlinear objects with strongly expressed mutual influences. It has been demonstrated that at a given supply voltage, the distribution of the current values of the phase currents in the quasi-steady-state mode makes it possible to estimate the situation in the arc space of an arc steelmaking furnace and identify the value of arc lengths. This dependence is preserved in transient modes. In order to identify arc lengths from the phase currents, it is proposed to use an approach based on the theory of fuzzy sets. The construction of the fuzzy arc length identifier rule base was carried out in two stages: the first stage used data from quasi-steady-state modes and the Wang–Mendel algorithm; the second stage involved adding a new rule to the database if the activity level of the formed rules was lower than the established level α for the data obtained in the dynamic mode. Further optimization of the parameters of the fuzzy identifier for operation in dynamic modes was carried out using the "back-propagation" algorithm. Based on the identified values of arc lengths, a control system for the movement of electrodes in an arc steelmaking furnace was synthesised. The proposed control system makes it possible to eliminate unproductive electrode movements due to changes in the situation in other phases of the arc steelmaking furnace and simplifies the application of modern methods of synthesising a control system for such complex objects. The results obtained in the mathematical model have confirmed the effectiveness of the proposed control system for the movement of electrodes in an arc steelmaking furnace. [ABSTRACT FROM AUTHOR]

Emotional controllers have been successfully pursued toward various control objectives in the past two decades, but there remain considerable challenges in exploiting their theoretical and cognitive aspects. This paper addresses these two challenges by proposing a modified thalamic connection based on an averaging operator using a radial basis emotional network (RBEN-ATC). From a theoretical perspective, we prove that the resulting RBEN-ATC mapping has the universal function approximation property. It also becomes continuous and differentiable with respect to the network weights. We then incorporate this structure to approximate the unknown dynamics of a nonlinear affine system with nonsymmetric input saturation and develop a stable adaptive radial basis emotional controller (ARBEC). From a cognitive perspective, we stay committed to the fundamental laws of the emotional brain. While this standpoint considerably complicates the design process, the resulting control system becomes interpretable from a biological perspective. For these purposes, a two-prong approach is employed here. Firstly, we incorporate a first-order compensator that handles the errors due to the nonsymmetric actuator saturation with unknown bounds. Secondly, we use a robust adaptive compensator that lessens the effect of uncertainties. Lyapunov stability analysis shows the overall ARBEC closed-loop stability of the nonlinear system. Furthermore, comparisons with other competing neuro- and fuzzy approaches show that ARBEC has better noise rejection and steady-state tracking while having comparable control energy consumption. [ABSTRACT FROM AUTHOR]

The paper deals with alliances and coalitions that can be formed by agents or entities. It is assumed that alliance agents cooperate and form coalitions for performing the tasks or missions. It is considered that alliance agents are unselfish. That is, they are more interested in achieving the common goal(s) than in getting personal benefits. In the paper, the concept of fuzzy alliance was introduced. A fuzzy alliance is considered as generalization of traditional alliance allowing agents to decide on the capabilities that their agents can and wanted deliver to coalition. Coalitions that can be formed by fuzzy alliance agents were considered. The definition of the "best" coalition was explained. The method of how to find the "best" coalition among all possible coalitions was suggested and verified by computer simulation. [ABSTRACT FROM AUTHOR]

To solve the problems of the slow response speed and poor adaptive capability of a permanent magnet synchronous motor (PMSM) under a fuzzy PI control system, a particle swarm optimization (PSO) fuzzy PI method is proposed as a parameter optimization control scheme in this paper. The proportion factor and quantization factor in fuzzy PI control are optimized through the iteration of a PSO algorithm. In addition, the parameters of the PI control are intelligently adjusted through the fuzzy control. A simulation model is developed using MATLAB/Simulink, and an experimental platform is constructed to verify the proposed algorithm. Test results demonstrate that the fuzzy PI control optimized by PSO improves the convergence accuracy of a system and reduces the speed ring overshoot to a minimum. Furthermore, the PSO-optimized fuzzy PI control exhibits characteristics such as small torque ripple, strong anti-interference capability, and fast dynamic response. [ABSTRACT FROM AUTHOR]

This article handles the problem of periodic event‐triggered control co‐design for the stabilization of Takagi‐Sugeno fuzzy models subject to stochastic deception attacks whose occurrence follows a given Bernoulli distribution. A novel delay‐dependent condition is presented to simultaneously design the event‐triggering mechanism and the state‐feedback controller to ensure the local mean‐square asymptotic stability of the closed‐loop system. The co‐design condition is derived as linear matrix inequalities by considering Lyapunov–Krasovskii stability arguments. In addition, an optimization procedure is provided to estimate an enlarged set of admissible initial conditions and the corresponding ultimate bound within the validity domain of the fuzzy model. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Summary: This article investigates the robust stability of permanent magnet synchronous generator (PMSG)‐based wind turbine (WT) systems with randomly occurring uncertainties (ROUs) under a fuzzy sampled‐data control scheme. To accomplish this, the norm‐bounded uncertainties in the d−q$$ d-q $$ inductance, system inertia, and stator resistance are considered, which are modeled as ROUs by using certain Bernoulli distributed white noise sequences. The nonlinear PMSG‐based WT systems can be expressed as local linear systems with a Takagi–Sugeno (T–S) fuzzy approach. To achieve stability performance against such kinds of uncertainties and external disturbances, fuzzy‐based sampled‐data control is designed for the proposed model. Then, the sufficient conditions are derived and expressed in the form of linear matrix inequalities based on looped‐Lyapunov functionals, which ensure the asymptotic stability of the closed‐loop T–S fuzzy model for the nonlinear PMSG–based WTs with an H∞$$ {H}_{\infty } $$ performance index. Finally, the derived conditions are validated with the proposed model and confirm the effectiveness of the proposed control scheme against the uncertainties and disturbances. Moreover, a comparison example is conducted to show the less conservative nature of the derived conditions. [ABSTRACT FROM AUTHOR]

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

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

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

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

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