International Journal of Fuzzy Systems最新文献

This paper studies the finite-time (H_infty ) security control problem based on a dynamic adaptive event-triggered mechanism (DAETM) for interval type-2 fuzzy networked control systems (NCSs) under deception attacks. Firstly, a DAETM is proposed to conserve network resources of NCSs. In contrast to existing DAETMs, the threshold of the proposed DAETM is determined founded on the average of the triggering error. This approach provides greater adaptability and flexibility in maintaining system control performance and conserving network communication bandwidth burden. Then, the closed-loop system is finite-time bounded and fulfills the desired (H_infty ) performance through constructing an appropriate Lyapunov function. Furthermore, the sufficient conditions for the existence of the finite-time ( H_infty ) fuzzy controller are proved. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed design approach.

Fuzzy Cognitive Maps (FCMs) have been attracting researchers from a wide application area due to being easy to apply and interpret. Since its proposal, the method has been improved to satisfy the diverse needs of practitioners such as solving different types of problems and representing particular types of uncertainty. The classical FCMs depend highly on the decision-maker judgments and the uncertainty inherent in the judgments deserves significant attention. Although there are several fuzzy extensions integrated into FCMs, the uncertainty caused by the lack of knowledge, the hesitancy of decision makers, and also the limited capacity of humans to deal with pre-defined rules should be considered. To address this issue, a new distance-based approach integrating Pythagorean Fuzzy Sets and FCMs is proposed. To the best of our knowledge, this is the first time this extension is integrated into FCMs. Besides allowing to represent the uncertainty until the end of the calculations, the new approach offers decision makers an easier and more flexible way to assess the strength of existing causal relationships. To provide a comparison between the proposed approach and the classical FCMs, two real-life applications are selected as case studies.

This paper concerns the stability and non-weighted (L_2)-gain analysis issues for the switched systems subject to asynchronous switching and external disturbance. By taking full advantage of the admissible edge-dependent average dwell time (AED-ADT) switching signal, a stability criterion of asynchronously switched systems is proposed to achieve a lower non-weighted (L_2)-gain index than the existing results. Furthermore, the dynamics of switched systems are described by using the polynomial fuzzy model instead of the T-S fuzzy model, which can represent a wider range of nonlinear plants. For such asynchronously switched polynomial fuzzy systems, the relaxed conditions are derived through the membership-function-dependent (MFD) technique and novel high-order multiple Lyapunov functions (MLFs) construction method to guarantee less conservative stability and non-weighted (L_2)-gain results. Finally, the superiority of the proposed approaches are verified through two simulation examples.

The nonsingular fast terminal sliding mode control (NFTSMC) problem of an uncertain robotic manipulator system based on the adaptive fuzzy wavelet neural network (AFWNN) is studied in this paper. First, a novel NFTSMC method is developed to achieve fixed-time tracking control of the robotic manipulator in the presence of parametric perturbation, external disturbances, and joint friction. Then, to address the adverse impacts of lumped uncertainties in the robotic manipulator system, a well-designed AFWNN algorithm is devised. Next, the tracking error system is proved to be fixed-time stable utilizing the Lyapunov function analysis method. Finally, the effectiveness and superiority of the NFTSMC strategy based on the AFWNN are verified by several comparative simulations.

Cyberattacks on frequency stability problems pose a significant threat to microgrids. Furthermore, frequency fluctuations brought on by cyberattacks in any region of the microgrid impact the system, endangering the stability of the network. This problem can be fixed using a self-adaptive virtual inertia control strategy, which enhances the microgrid’s stability and damping capabilities. When using this control method, the evaluation of the microgrid’s frequency data is obtained through the usage of a phase-locked loop (PLL). However, because of its system dynamics, PLL implementation in microgrids produces more frequency oscillation. Once more, this work addresses the above problem by presenting a robust adaptive fuzzy PID controller (AFPID) for virtual inertia control. This controller will simultaneously increase the stability of the microgrid system and reduce the undesired frequency measurement impacts caused by cyberattacks. A unique modified artificial ecosystem-based optimization technique (mAEO) is put forth to fine-tune the controller parameters further. The effectiveness of the suggested solutions is examined by contrasting the simulation results with a few common strategies while considering changes in system parameters and varying rates of cyberattacks.

This paper investigates the dissipativity analysis problem of discrete-time singular interval type-2 (IT-2) fuzzy Markov jump systems (FMJSs) with actuator faults and time-vary delays. First, considering the influence of actuator faults, a method employing Lyapunov function dependent on system modes and fuzzy bases is proposed. Second, based on the linear matrix inequality (LMI), a stability criterion related to time delay is established using the discrete Jensen inequality and delay partitioning technique. Additionally, addressing the mismatched membership functions (MFs) between the system fuzzy model and the controller, this paper adopts a membership function dependency approach, effectively resolving the problem of mismatched premise variables between the fuzzy model and the controller. Finally, the effectiveness of this method is validated through an illustrative example.

It is always challenging for us to process uncertain information. While the proposed multi-granular unbalanced hesitant fuzzy linguistic term set (MGUHFLTS) can handle multi-granular information and the asymmetric distribution of linguistic terms in real decision problems, it treats all hesitant memberships as equally significant, which is clearly inconsistent with reality. To address this deficiency, we introduce a new concept: D-multi-granular unbalanced hesitant fuzzy linguistic term set (D-MGUHFLTS). This concept combines D numbers’ ability to deal with uncertainty with MGUHFLTS’s capability to handle unbalanced and multi-granular linguistic information, enriching the representation of uncertain information. Additionally, we propose comparison rules, operational rules, and aggregation operators of the D-MGUHFLTS. Subsequently, we develop a decision-making method grounded in D-MGUHFLTS and apply it to a real-life scenario pertaining to online doctors’ rankings, showcasing the validity and flexibility of D-MGUHFLTS in processing uncertain information. Furthermore, by comparing it with other methods, we further illustrate the effectiveness and applicability of the proposed method.

This paper proposes an event-triggered finite-time adaptive fuzzy tracking control for the quadrotor unmanned aerial vehicle (UAV) subjected to time-varying output constraints and external disturbances. The challenge of the event-triggered control design is how to make the output of a quadrotor UAV not exceed the prescribed time-varying constraints and achieve finite-time tracking. By redefining the nonlinear terms and control inputs, the quadrotor UAV system is transformed into multiple input multiple output (MIMO) nonlinear system with pure-feedback structure to facilitate the tracking controller design of position and attitude. To deal with time-varying output constraint, the tangent barrier Lyapunov functions (TBLFs) are introduced in the adaptive finite-time controller design process. To save the computation, resources, and transmission load, the event-triggered mechanisms are applied to control inputs, and the closed-loop stability of quadrotor UAV system can be proved. Finally, the simulation results are given to indicate the validity of the presented control strategy.

The rise of autonomous vehicles has become a key indicator of smart city development. Unlike traditional cars, which are fully operated by humans, autonomous vehicles rely on sensors to collect data about their surroundings for safe navigation. Due to their reliance on electricity rather than fossil fuels, autonomous cars have a reduced environmental impact in terms of greenhouse gas emissions. However, the susceptibility of autonomous cars to cyberattacks poses a risk to both the vehicles and human lives. Consequently, this study aims to identify and differentiate anomalies in real-time sensor readings of autonomous vehicles. Initially, a fuzzy logic controller with two inputs and one output was fine-tuned to serve as the base controller. Subsequently, data were collected to train the ANFIS-based controllers, each of which was evaluated using three simulations: step response, sine wave response, and random response. The PSO-ANFIS was used to generate an anomaly dataset by introducing artificial false data, and the ensemble model demonstrated exceptional performance, achieving a 99.99% accuracy in classification.

This paper investigates the fixed-time predefined performance control problem for stochastic nonlinear systems with dead-zone input and unmeasurable states. By designing a fixed-time performance function (FTPF), the expected performance indexes can be obtained within fixed time. Combining dead-zone inverse technique with error transformation method, a model which contains a linear and a disturbance-like term is established to handle dead-zone nonlinearity. Subsequently, an observer-based fixed-time predefined performance controller is constructed by utilizing FTPF, which ensures that all variables in the closed-loop system are bounded in probability. Simultaneously, the tracking error of this paper converges to a predefined region in fixed time. Finally, the effectiveness of the presented control scheme is verified by two simulation examples.