International Journal of Fuzzy Systems最新文献

The acquisition of decision rules in multi-intuitionistic fuzzy decision information systems is both challenging and important. To address this issue, it is necessary to combine decision rules from various systems to obtain a more reliable decision rule. Additionally, the use of three-way decisions can help determine the optimal decision value. In this research article, we explore the decision problems of multi-intuitionistic fuzzy decision information systems by utilizing the D-S evidence theory and three-way decisions. We start by providing an overview of the belief structure of intuitionistic fuzzy sets. Then, we propose a fused mass function of decision rules that assists in obtaining satisfactory or optimal decision value sets through three-way decisions. To facilitate the fusion of decision value sets, we present an algorithm that effectively integrates them. Furthermore, we provide examples to illustrate the algorithm and demonstrate the effectiveness and efficiency of our proposed approach.

This paper aims to propose a more efficient control algorithm for improving the control performance of uncertain nonlinear systems. A novel improved Takagi–Sugeno–Kang (TSK) wavelet type-2 fuzzy brain emotional learning controller (ITSKWT2FBC) is proposed. The proposed ITSKWT2FBC contains a brain emotional learning controller (BELC) combined with an improved TSK wavelet type-2 fuzzy system. By taking the advantages of BELC and the improved wavelet type-2 TSK fuzzy system, the control performance of this controller can be improved. The adaptive laws of controller parameters are derived from the defined Lyapunov function and gradient descent method. Moreover, the system stability can be guaranteed based on the Lyapunov stability theory. Finally, three nonlinear systems, a double inverted pendulum system, a Mackey–Glass time series prediction (TSP) and a four dimensional (4D) chaotic system synchronization are simulated to illustrate the effectiveness of the proposed method. The simulation results have demonstrated the effectiveness of the developed method.

In this article, a decentralized event-triggered scheme (DETS)-based model predictive control (MPC) strategy is investigated for a nonlinear cyber-physical system (CPS) under jamming attacks. The nonlinear plant is described by an interval type-2 Takagi-Sugeno fuzzy model and the system measurements are grouped into several nodes. First, a DETS is proposed to determine whether the measured signals are transmitted. By the DETS, the measured signals are released asynchronously, which are more flexible. Moreover, the jamming attacks are supposed to be energy limited in the multiple wireless transmission channels. Under jamming attacks, the signal-to-interference-plus-noise ratio will be degraded and the measured signals will be lost. To maximize the damaging effect, an optimal power allocation strategy of jammer is designed. Considering that the system states are unmeasurable, an observer-based MPC algorithm is proposed, which consists of an off-line designed secure state observer and an on-line optimized MPC strategy. Besides, the recursive feasibility of presented algorithm and the stability of fuzzy CPS are ensured. Finally, two illustrative examples are given to show the validity and superiority of proposed method.

With the fast-growing demand for wind energy, small wind turbines are becoming a viable and cost-effective source of clean and renewable electricity for residential, businesses, small industries, and remote applications. To harness the maximum power, wind turbines (WT) should be operated at full capacity; hence, it is crucial to design an effective maximum power point tracking (MPPT) control scheme. This paper presents an effective MPPT control algorithm based on fuzzy logic control (FLC) which tracks the slope of the mechanical power of the WT as a function of the rotational speed. The output of this MPPT generates the duty cycle for the DC–DC converter to allow the WT to extract the maximum power under a wide range of wind speed and loading conditions. The electrical generator used in the studied wind energy conversion system is the permanent magnet synchronous generator (PMSG). The proposed FLC-MPPT strategy is simple to implement and does not require any prior knowledge of the WT parameters. The proposed FLC-MPPT technique for WT is simulated using MATLAB/Simulink and validated experimentally on a laboratory setup using a dSPACE1104 board. In addition, a comparison between the proposed FLC-MPPT and other methods under significant wind speed fluctuations and variable load conditions is presented to demonstrate its effectiveness. The results show that the proposed MPPT has a fast convergence with a high tracking accuracy and exhibits low steady-state oscillations.

This paper proposes a fuzzy control technique for switched port-controlled Hamiltonian systems, in which input saturation and completely unknown internal dynamics occur. Based on the mean-value theorem, multiple Lyapunov functions scheme, and universal approximation ability of fuzzy logic systems, a switching fuzzy adaptive controller is designed to ensure that all closed-loop signals are semiglobally uniformly ultimately bounded. Furthermore, the norm of an ideal weighting vector in fuzzy logic systems is taken as the estimation parameter instead of the elements of the weighting vector to reduce the dimension of adaptation laws. This switching controller can weaken the conservativeness brought by the application of the same controller for each subsystem. Finally, some examples are provided to verify the validity of the proposed approach.

This paper deals with the study of controllability and observability of fuzzy fractional descriptor dynamical system in terms of granular differentiability. The granular fuzzy solution for granular descriptor fractional dynamical system (GrDFDS) is obtained by using the Mittag–Leffler function and granular Laplace transform and the solution is represented in terms of the state transfer matrix. The controllability and observability of GrDFDS is analysed with the help of theorems using the controllability Gramian matrix and observability Gramian matrix. In order to illustrate the efficacy of our findings, we hereby give the controllability analysis of an engineering problem pertaining to the compatibility of a descriptor fractional electric circuit. The graph visually represents the outcome, indicating that the granular descriptor dynamical system of the electric circuit can be effectively controlled, leading to the successful transition of its state from the granular initial to the final state.

In this paper, we propose a novel adaptive T–S fuzzy sliding mode controller (ATSFSMC) and apply it to robot arm trajectory tracking. Since the robot arm belongs to a highly nonlinear system model, we use T–S fuzzy sliding mode control (TSFSMC) as the main controller, and consider the problems encountered in the actual solution of Linear matrix inequalities (LMI), then propose a split system matrix method to effectively solve the problem. In order to ensure that the controller has the best control parameters, we propose a new Lyapunov function design method, so that the state gain parameters in TSFSMC can be adjusted to the best value by the adaptive law, and the controller can handle the unknown disturbances and uncertainties of the system. Finally, we prove the stability of the system and demonstrate the excellent performance of the controller through simulation and experimental results.

This paper focuses on the combined path following (PF) and direct yaw-moment control (DYC) problem of unmanned electric vehicles (UEVs) based on an event-triggered Takagi–Sugeno (T–S) fuzzy method. First, to describe the parameter uncertainties and nonlinear tire dynamics of UEVs, a T–S fuzzy dynamic model is developed for performance analysis and controller design of the vehicle lateral dynamic system. Second, as the signal transmission inevitably suffers from delay and data dropouts because of the limited network bandwidth, an event-triggered scheme is constructed to complete the communication scheduling and save the communication resource. Third, considering that there is an unavoidable asynchronous constraint between the fuzzy system and the fuzzy controller due to the communication delay, a hierarchical event-triggered fuzzy controller design approach is given via a new Lyapunov functional. Moreover, a differential distribution scheme is introduced to obtain the desired torque of each tire of the vehicle. Finally, experimental results validate that the presented controller in this paper is able to improve the path following accuracy by 61.93% with a resource economization 92.04% compared with the existing method.

In this article, the expert evaluations transformed in the multi-attribute decision-making (MADM) model are presented in the discrimination q-rung picture linguistic fuzzy numbers (q-RPLFNs). In the construction of a second-order additive fuzzy measure (TOAFM) the attributes’ interaction indexes and Shapley values are taken into account. The Shapley entropy maximum principle for identification of associated probabilities class (APC) of a TOAFM is constructed. Based on the APC of the TOAFM, a new aggregation operators’ class is constructed which represents some hybrid extensions of ordered weighted averaging (OWA), geometric (OWG), the Choquet integral averaging (CA) and geometric (CG) operators under discrimination q-rung orthopair fuzzy (q-ROF) and q-rung picture linguistic fuzzy (q-RPLF) information. These operators, constructed for the q-RPLF and q-ROF environments, take into account the overall pair interactions among attributes. Main properties on the correctness of extensions are proved: for the lower and upper capacities of order 2, all constructed operators consequently coincide with q-ROF and q-RPLF Choquet averaging and geometric operators, respectively. Constructed operators in the evaluation of prediction of fuzzy Collaborative Filtering Recommender Systems (CFRS) are used. New symmetric discrimination measures as some extensions of discrimination measures for the fuzzy CFRS are proposed. Users’ profile data by the constructed operators in the new similarity measure under q-rung picture linguistic environment are aggregated. The developed new approach is schematically described in such a way that it can be “embedded” in any existing CFRS model. An example is given to illustrate the results, for which the software designed to aggregate profile data for similarity comparison provides the use of new and well-known classical aggregation operators.

This paper introduces a fuzzy intermittent control issue for nonlinear PDE-ODE coupled system under spatially point measurements (SPMs), which can be represented by an ordinary differential equation (ODE) and a partial differential equation (PDE). Firstly, the nonlinear coupled system is aptly characterized by the Takagi–Sugeno (T–S) fuzzy PDE-ODE coupled model. Subsequently, based on T–S fuzzy model, a novel Lyapunov function (LF) is provided to design a fuzzy intermittent controller ensuring exponential stability of the closed-loop coupled system. The stabilization conditions are presented by means of a group of space-dependent linear matrix inequalities (SDLMIs). Finally, simulation results are given to illustrate the effectiveness of the proposed design method in the control of a hypersonic rocket car (HRC).