Logic System Research Articles

An Analysis of Performance Appraisal in China from The Perspectives of Scholars

The importance of performance appraisal is widely acknowledged, and its implementation varies from country to country. China's performance evaluation is conducted once a year, and a new version of the civil service evaluation system will be developed in 2020. This study examines academic perspectives on performance appraisal in China, which often vary in approach, content, incentive structures, and outcomes. Due to the lack of accuracy and subjectivity, the concept of "unscientific indicator design" in China has been greatly criticized. Chinese civil servants must have a high ideological and political quality, strong working ability, maintain a good work style, outstanding work performance, and achieve excellent results in the annual assessment. These criteria have a wide range and have the potential to lead to unfair evaluations that can negatively impact employee motivation. Many experts put forward opinions and suggestions on improving the content of performance appraisal in China; The Dutch academics suggest a transparent and detailed workflow system for registering and measuring performance, while the Brazilian academics suggest that employee performance should be taken seriously during evaluation. This requires recognizing strengths and weaknesses, making suggestions for improvement, and identifying their career prospects based on the results of the performance review. This study mainly examines the scholars' views on the similarities and differences of performance evaluation. In addition, it assesses current concerns within the appraisal system. This data will provide the basis for the exploration and development of broader, more scientific and logical performance evaluation systems.

Towards Enhanced Autonomous Driving Takeovers: Fuzzy Logic Perspective for Predicting Situational Awareness

This paper investigates the application of fuzzy logic to enhance situational awareness in Advanced Driver Assistance Systems (ADAS). Situational awareness is critical for drivers to respond appropriately to dynamic driving scenarios. As car automation increases, monitoring situational awareness ensures that drivers can effectively take control of the vehicle when needed. Our study explores whether fuzzy logic can accurately assess situational awareness using a set of 14 critical predictors categorized into time decision, criticality, eye-related metrics, and driver experience. We based our work on prior research that used machine learning (ML) models to achieve high accuracy. Our proposed fuzzy logic system aims to match the predictive accuracy of ML models while providing additional benefits in terms of interpretability and robustness. This approach emphasizes a fresh perspective on situational awareness within ADAS, potentially improving safety and efficiency in real-world driving scenarios.

A hybrid model of artificial intelligence integrated into GIS for predicting accidents in water supply networks

The search for an effective and reliable model for predicting accidents on water supply networks by determining their exact locations has always been important for effectively managing water distribution systems. This study, based on the adaptive neuro-fuzzy logical inference system (ANFIS) model, was developed to predict accidents in the city of Kyiv (Ukraine) water supply network. The ANFIS model was combined with genetic algorithms and swarm optimization (ACO) methods and integrated into a GIS to visualize results and determine locations. Forecasts were evaluated according to the following criteria: mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). Depending on the amount and type of input data, ANFIS optimization with genetic algorithms and swarm optimization (ACO) can, on average, increase the accuracy of ANFIS predictions by 10.1% to 11%. The obtained results indicate that the developed hybrid model may be successfully applied to predict accidents on water supply networks.

Advanced Method for Improving Marine Target Tracking Based on Multiple-Plot Processing of Radar Images

Advancements in technology have led to the development of high-resolution radars that provide highly detailed images of targets over a wide field of view. These radar images can significantly improve filtering and tracking accuracy, especially in marine environments. However, traditional methods like the binary and barycentric methods are inadequate, as they do not capture critical information for tracking targets, such as direction. Therefore, in this article, a new method for improving the estimation of target characteristics to improve tracking accuracy based on the processing of high-resolution radar images is proposed. The proposed method consists of three modules. Firstly, the radar images of the target are decomposed into layers to determine local maximum regions and to estimate target characteristics such as reflected energy and area and the centroids of plots. In the second module, the plots are grouped using a fuzzy logic system. The inputs of the fuzzy logic system include the above-estimated parameters of the targets. The output is the chance that the plot is at the center of the target. The plots with the highest chances are considered target centers, and the other plots are grouped into their respective target. At the end, the true target center is recalculated. This process is called modified fuzzy C-means (FCM-M). In the last stage, the estimated target center coordinates are fed into a Kalman filter (KF) to solve filtering and tracking problems. The performance is evaluated using a measured radar dataset. The experimental results show that the proposed method performs better than traditional methods based on binary image processing. Additionally, the proposed method offers extra information about the targets, such as their direction, the energy of each reflected part, and the area, which traditional methods does not provide.

Parameter Prediction with Novel Enhanced Wagner Hagras Interval Type-3 Takagi–Sugeno–Kang Fuzzy System with Type-1 Non-Singleton Inputs

This paper presents the novel enhanced Wagner–Hagras interval type-3 Takagi–Sugeno–Kang fuzzy logic system with type-1 non-singleton inputs (EWH IT3 TSK NSFLS-1) that uses the backpropagation (BP) algorithm to train the antecedent and consequent parameters. The proposed methodology dynamically changes the parameters of only the alpha-0 level, minimizing some criterion functions as the current information becomes available for each alpha-k level. The novel fuzzy system was applied in two industrial processes and several fuzzy models were used to make comparisons. The experiments demonstrated that the proposed fuzzy system has a superior ability to predict the critical variables of the tested processes with lower prediction errors than those produced by the benchmark fuzzy systems.

Energy-Efficient, Cluster-Based Routing Protocol for Wireless Sensor Networks Using Fuzzy Logic and Quantum Annealing Algorithm.

The main limitation of wireless sensor networks (WSNs) lies in their reliance on battery power. Therefore, the primary focus of the current research is to determine how to transmit data in a rational and efficient way while simultaneously extending the network's lifespan. In this paper, a hybrid of a fuzzy logic system and a quantum annealing algorithm-based clustering and routing protocol (FQA) is proposed to improve the stability of the network and minimize energy consumption. The protocol uses a fuzzy inference system (FIS) to select appropriate cluster heads (CHs). In the routing phase, we used the quantum annealing algorithm to select the optimal route from the CHs and the base station (BS). Furthermore, we defined an energy threshold to filter candidate CHs in order to save computation time. Unlike with periodic clustering, we adopted an on-demand re-clustering mechanism to perform global maintenance of the network, thereby effectively reducing the computation and overhead. The FQA was compared with FRNSEER, BOA-ACO, OAFS-IMFO, and FC-RBAT in different scenarios from the perspective of energy consumption, alive nodes, network lifetime, and throughput. According to the simulation results, the FQA outperformed all the other methods in all scenarios.

Hoping the best, expecting the worst: Forecasting forest fire risk in Algeria using fuzzy logic and GIS

Forest fires pose severe threats to ecosystems and communities globally, especially in vulnerable semi-arid regions like North Africa. Understanding the key factors influencing forest fire dynamics is essential for effective management and mitigation. This study aims to comprehensively analyze forest fire risk patterns in Djebel El Ouahch's massif (Algeria), focusing on integrating bioclimatic, fuel, geomorphological, and human factors through advanced fuzzy logic and geographic information system (GIS) techniques. Climatic station data, satellite imagery, and GIS were employed to map bioclimatic parameters, land cover, and geomorphological features. Fuzzy logic systems were applied to integrate these factors, assigning appropriate weights based on their significance. The resulting forest fire prediction model was defuzzified to generate predictive maps indicating varying vulnerability levels within the study area. Predictive maps delineated areas of low to high forest fire risk. Low-risk zones were characterized by sparse vegetation, while high-risk regions featured densely vegetated slopes near human settlements. The study identified critical factors influencing vulnerability, emphasizing the impact of climate, terrain, and human activities. Urgent attention was directed toward high-risk areas, necessitating tailored fire prevention measures and strategic urban planning to minimize human-induced risks. The results underscored the complex interaction of natural and anthropogenic factors in shaping forest fire susceptibility. Understanding these dynamics facilitates evidence-based policymaking, enhancing forest fire preparedness, biodiversity preservation, and community safety. Additionally, the study emphasized the need for continuous research incorporating real-time climate data and socio-economic factors to refine predictive models. This research provided valuable insights into forest fire risk patterns in Djebel El Ouahch, serving as a foundation for targeted fire management strategies. By bridging the gap between theoretical knowledge and practical application, this study contributes significantly to sustainable forest management and disaster mitigation efforts globally, emphasizing the importance of proactive measures in safeguarding vulnerable ecosystems and communities.

A New Fuzzy Bayesian Inference Approach for Risk Assessments

Bayesian network (BN) inference is an important statistical tool with additive symmetry. However, BN inference cannot deal with the uncertain, fuzzy, random, and conflicting information from experts’ knowledge in the process of conducting a risk assessment. To tackle this issue, a new fuzzy BN inference approach for risk assessments was proposed based on cloud model (CM), interval type-2 fuzzy set (IT2 FS), interval type-2 fuzzy logic system (IT2 FLS), modified Dempster–Shafer (D-S) evidence theory (ET), and Latin hypercube sampling (LHS) methods along the following lines. Firstly, CM was integrated into IT2 FS, and CM-based IT2 FS (CM-IT2 FS) was defined in the IT2 FLS. Secondly, modified D-S ET was utilized to determine the CM-IT2 FS-based a priori probabilities, and the CM-IT2 FS-based BN model was established. Thirdly, the CM-IT2 FS-based a priori probabilities were reduced to the CM-IT1 FS-based ones using a type reducer in the IT2 FLS, LHS was applied to propose a new fuzzy BN inference algorithm, and then, the new algorithm was used in a typical case to perform the fuzzy BN positive inference for risk prediction and the fuzzy BN reverse inference for risk sensitivity analysis. Finally, the BN inference results were analyzed using the proposed algorithm and the two common BN inference algorithms, and the effectiveness of the proposed approach was validated. It can be concluded that the proposed approach was both accurate and promising.

ГРАФИЧЕСКИЕ СРЕДСТВА ОПЕРАЦИОННЫХ СИСТЕМ

The methods of processing multimedia and graphical information in operating systems are studied using the example of MCI, API and GDI interfaces of Windows operating system. The interfaces of GDI and GDI+ graphics devices are examined in detail. The capabilities and features of the OpenGL graphics library, which is commonly used in the Windows operating system, are presented. The principles of operation of the GDI interface are given. Three types of drawing tools supported by the context of the GDI interface are examined in detail: pen, brush and font. The stages of coordination of logical and physical coordinate systems are considered. The OpenGL library graphics elements (bits arrays, polygons, segments, polylines, etc.), as well as basic geometric objects and a set of polyhedra: cube, cone, torus, octahedron, icosahedron, dodecahedron and tetrahedron are considered. As an example of OpenGL library usage, a three-dimensional graphics editor was studied. It works with three-dimensional objects of various types and allows reproduction of an object in two modes (lines and a solid surface), as well as performance of following operations with a graphic object: deformation, rotation, transpositionong along axes, loading and saving on hard drive.

Visualized lateral flow assay for logic determination of co-existing viral RNA fragments

Different types of pathogenic viruses that have common transmission path can be co-infected, inducing distinct disease procession in comparison to that infection of one. Also, in the post COVID-19 time, more types of respiratory infectious virus are becoming prevalent and are concurrent. Those bring an urgent need for detection of co-existing viruses. Here, we propose a visualized lateral flow assay for logic determination of co-existing viral RNA fragments. In the presence of specific viral RNA inputs, DNAzyme is de-blocked according to defined logic, and catalyzes the hydrolysis of hairpin-structural substrate. One of cleaved substrates contains DNAzyme domain to realize dual signal amplification, which obtains copious of the other cleaved substrates. The cleaved substrates act as linking strands for bridging DNA-modified gold nanoparticles onto lateral flow strip to induce coloration on test line. “AND”, “OR” and “INHIBIT” controlled lateral flow assays are respectively demonstrated for co-existing viral RNA detection, and the visual results can be obtained by the same kind of prepared strip, without need of re-fabricating strips according to logic systems. The work provides a flexible, convenient, visual and logic-processing strategy for simultaneous analysis of co-existing viruses.

Interval type-2 fuzzy stochastic configuration networks for soft sensor modeling of industrial processes

Soft sensors have been widely applied to predict key variables that are difficult to measure for industrial process modeling. In this paper, a novel randomized interval type-2 fuzzy neural network with parallel learning, called IT2F-PSCN, is presented for soft sensor modeling of industrial processes. It trains the upper and lower bounds of the interval type-2 fuzzy logic system separately to facilitate type reduction, thereby integrating the fuzzy logic system with stochastic configuration networks. To achieve the appropriate structure and parameters of the model, we develop a two-phase training scheme. In the first phase, a sparse rule interpolation method with stochastic configuration is applied to generate new fuzzy rules. In the second phase, the hidden layer is constructed through parallel stochastic configuration to enhance the nonlinear representational capacity. The validity of IT2F-PSCN is confirmed by a series of experiments, including four benchmark data modelings, simulation on the Tennessee Eastman process, and soft sensor modeling for the slurry grade of the first rougher in a zinc flotation process. The experimental results indicate that the proposed IT2F-PSCN performs favorably compared with other methods.

Competition of decoherence and quantum speed limits for quantum-gate fidelity in the Jaynes-Cummings model

Quantum computers are operated by external driving fields, such as lasers, microwaves, or transmission lines, that execute logical operations on multiqubit registers, leaving the system in a pure state. However, the drive and the logical system might become correlated in such a way that, after tracing out the degrees of freedom of the driving field, the output state will not be pure. Previous works have pointed out that the resulting error scales inversely with the energy of the drive, thus imposing a limit on the energy efficiency of quantum computing. In this study, focusing on the Jaynes-Cummings model, we show how the same scaling can be seen as a consequence of two competing phenomena: the entanglement-induced error, which grows with time, and a minimal time for computation imposed by quantum speed limits. This evidence is made possible by quantifying, at any time, the computation error via the spectral radius associated with the density operator of the logical qubit. Moreover, we also prove that, in order to attain a given target state at a chosen fidelity, it is energetically more efficient to perform a single driven evolution of the logical qubits rather than to split the computation in subroutines, each operated by a dedicated pulse. Published by the American Physical Society 2024

Implementation and Possibilities of Fuzzy Logic for Optimal Operation and Maintenance of Marine Diesel Engines

This paper explores the implementation and possibilities of utilizing fuzzy logic theory for optimal operation and early fault detection in marine diesel engines. It emphasizes its role in managing the complexity and ambiguity inherent in engine performance and preventive maintenance. Preventive maintenance is crucial for ensuring the reliability and longevity of marine diesel engines. Implementing fuzzy logic control (FLC) systems can enhance the preventive maintenance strategies for these engines, leading to reduced downtime, lower maintenance costs, and improved overall performance. Through a comprehensive literature review and analysis of a case study, this paper demonstrates the adaptability, effectiveness, and transformative potential of fuzzy logic systems. Focusing on applications such as engine speed control, performance improvements, and early fault detection, the paper highlights the implementation of fuzzy logic for enhanced predictive capabilities. The study aims to offer a flexible approach to engine management through fuzzy logic, laying the way for significant improvement in optimal marine diesel engine operation.

Event-triggered finite-time fuzzy tracking control for a time-varying state constrained quadrotor system based on disturbance observer

In contrast with most existing results concerning quadrotor unmanned aerial vehicle (UAV) wherein time-triggered controllers or only symmetric state constraints are considered, this paper deals with the event-triggered finite-time trajectory tracking control problem for quadrotor UAV with asymmetric time-varying state constraints and unknown external disturbances. Firstly, the fuzzy logic system is used to approximate the nonlinear relationship of the quadrotor UAV system, and a disturbance observer is designed to compensate for composite disturbances. Secondly, the command filter and the event-triggered mechanism are introduced into the controller design, while asymmetric time-varying Lyapunov functions are constructed. Thus, the computational complexity and communication burden are reduced, and the event-triggered finite-time adaptive fuzzy controller is guaranteed to stabilize the quadrotor UAV in finite-time without violating all asymmetric time-varying state constraints. Finally, the simulation results demonstrate the validity of the proposed control strategy.

Which ‘Intensional Paradoxes’ are Paradoxes?

We begin with a brief explanation of our proof-theoretic criterion of paradoxicality—its motivation, its methods, and its results so far. It is a proof-theoretic account of paradoxicality that can be given in addition to, or alongside, the more familiar semantic account of Kripke. It is a question for further research whether the two accounts agree in general on what is to count as a paradox. It is also a question for further research whether and, if so, how the so-called Ekman problem bears on the investigations here of the intensional paradoxes. Possible exceptions to the proof-theoretic criterion are Prior’s Theorem and Russell’s Paradox of Propositions—the two best-known ‘intensional’ paradoxes. We have not yet addressed them. We do so here. The results are encouraging. §1 studies Prior’s Theorem. In the literature on the paradoxes of intensionality, it does not enjoy rigorous formal proof of a Gentzenian kind—the kind that lends itself to proof-theoretic analysis of recondite features that might escape the attention of logicians using non-Gentzenian systems of logic. We make good that lack, both to render the criterion applicable to the formal proof, and to see whether the criterion gets it right. Prior’s Theorem is a theorem in an unfree, classical, quantified propositional logic. But if one were to insist that the logic employed be free, then Prior’s Theorem would not be a theorem at all. Its proof would have an undischarged assumption—the ‘existential presupposition’ that the proposition ∀p(Qp→¬p)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\forall p(Qp\\!\\rightarrow \\!\\lnot p)$$\\end{document} exists. Call this proposition ϑ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\vartheta $$\\end{document}. §2 focuses on ϑ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\vartheta $$\\end{document}. We analyse a Priorean reductio of ϑ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\vartheta $$\\end{document} along with the possibilitate ◊∀q(Qq↔(ϑ↔q))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Diamond \\forall q(Qq\\!\\leftrightarrow \\!(\\vartheta \\!\\leftrightarrow \\! q))$$\\end{document}. The attempted reductio of this premise-pair, which is constructive, cannot be brought into normal form. The criterion says we have not straightforward inconsistency, but rather genuine paradoxicality. §3 turns to problems engendered by the proposition ∃p(Qp∧¬p)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\exists p(Qp\\wedge \\lnot p)$$\\end{document} (call it η\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\eta $$\\end{document}) for the similar possibilitate ◊∀q(Qq↔(η↔q))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Diamond \\forall q(Qq\\!\\leftrightarrow \\!(\\eta \\!\\leftrightarrow \\! q))$$\\end{document}. The attempted disproof of this premise-pair—again, a constructive one—cannot succeed. It cannot be brought into normal form. The criterion says the premise-pair is a genuine paradox. In §4 we show how Russell’s Paradox of Propositions, like the Priorean intensional paradoxes, is to be classified as a genuine paradox by the proof-theoretic criterion of paradoxicality.

Adaptive fixed-time fuzzy fault-tolerant control for robotic manipulator with unknown friction and composite actuator faults

This paper exploits the issue of fixed-time (FT) tracking control (TC) for uncertain robotic manipulator systems (RMS) with composite actuator faults (CAFs), unknown disturbances and joint friction. The fuzzy logic system (FLS) algorithm is introduced to fit the unknown uncertainties including unmodeled parameters, CAFs and joint friction of the RMS, after which a relevant updating algorithm is employed to generate the unidentified boundary of the plant disturbance and FLS approximation error. Then a novel adaptive fuzzy sliding mode (SM) fault-tolerant controller is synthesized, which not only guarantees the boundedness of estimation errors of the involved adaptive parameters, but also actualizes the reachability of the predevised sliding surface. Furthermore, the proof that the system trajectories can achieve the FT convergence to the expected position signals, is then provided on account of the FT stability theory. Ultimately, simulation experiments exhibit the rationality based upon the current control algorithm.

A fractional-order multiple-model type-2 fuzzy control for interconnected power systems incorporating renewable energies and demand response

Frequency regulation in Multi-region Interconnected Power Systems (MIPS), incorporating wind turbine systems, energy storage units, and demand response, is a challenging control problem. The problem involves maintaining grid stability, integrating variable renewable energy sources, enhancing grid resilience, optimizing energy storage and demand response capabilities, and ensuring regulatory compliance. Effective frequency regulation is a key problem for reliable and sustainable power system operation. In this paper, complex and uncertain dynamics in various components of a MIPS are modeled using multiple first-order dynamic fractional-order Type-2 Fuzzy Logic Systems (T2-FLSs). The models are evaluated, and the best possible dynamic model is chosen. With the best possible model, an optimal controller is designed. The stability and optimality analysis are presented through a Linear Matrix Inequality (LMI) approach. The adaptive laws of T2-FLSs are derived such that some LMI-based conditions are satisfied. The designed controller does not depend on the dynamics of MIPS. The uncertainties of time-varying load, wind energy, and solar power are modeled using T2-FLSs. The suggested LMI technique presents adaptation laws for T2-FLSs to ensure stability in the presage of natural disturbances and errors. The designed scheme is validated by applying to a practical 39-bus IEEE test system that includes the wind farms, demand response, and energy storage systems. The simulation results under various conditions verify the usefulness of the suggested controller. The comparisons with conventional controllers demonstrate that the suggested approach is more effective under uncertainties and natural perturbations.

Disturbance Observer and Adaptive Control for Disturbance Rejection of Quadrotor: A Survey

Quadrotors are widely applied in many fields, but they often face various external disturbances in actual operation. This makes it necessary to design a controller that can handle disturbances. Disturbance observer and adaptive control techniques are commonly used disturbance rejection techniques, the core idea of which is to estimate the disturbances in real time and incorporate the estimated values into the controller to suppress the disturbances. In this paper, various disturbance observers and adaptive control techniques, including nonlinear disturbance observers, extended state observers, neural networks, and fuzzy logic systems, are introduced, along with their variants or different structures. These techniques improve the adaptability and robustness of quadrotors to complex environments. Finally, future research directions for the disturbance rejection of quadrotors are also presented.

A logic of higher-order preferences

If an agent prefers one kind of agents to the other agents, then the agent has first-order preferences. If the agent prefers agents with one kind of preferences to the other agents, then the agent has second-order preferences. The article proposes a sound, complete, and decidable logical system capable of expressing higher-order preferences.

Adaptive fuzzy fixed‐time tracking control of nonlinear systems with unmodeled dynamics

SummaryThe 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.