Search Results (225)

Currently, most existing traditional teaching evaluation models are difficult to truly reflect the contribution and guiding role of administrative policies in teaching decision-making, and lack the interpretability of teaching semantics in assessing actual teaching methods and effectiveness. In addition, the substantive strategies of intervention are also difficult to quantify and evaluate. This study proposes a hierarchical Takagi–Sugeno–Kang (TSK) fuzzy classification model (Pgt-TC) with positive intervention guidance ability. The study stacks several interpretable zero-order TSK fuzzy classifiers as the basic training units (BTUs), ensuring that the final model has high interpretability of teaching semantics. Firstly, the fuzzy rule base corresponding to BTUs is determined according to the contribution level of each rule, and residual is used to reduce the error interference between adjacent BTUs, achieving the goal of improving the generalization ability of the training model. In addition, this study designed a strategy to generate posterior parameters by solving the approximation error of adjacent BTUs, which improved the classification and generalization performance of the fuzzy system. The proposed model is evaluated on five datasets, including one core educational research dataset (177 samples, 19 features) and four UCI benchmark datasets (163–5000 samples, 4–27 features). Results are reported using 5-fold cross-validation with mean values. Experimental results show that Pgt-TC achieves competitive classification performance across all datasets. On the educational dataset, it attains an average test accuracy of 93.47 and an average test SE value of 98.60, while also offering interpretability to explain educational intervention decisions. Full article

This paper proposes a novel data-driven K-Gap metric method based on the Koopman operator for the detection and isolation of multiplicative faults in high-speed train suspension systems. A systematic comparison is conducted with a data-driven K-Gap approach implemented through the fuzzy modeling framework. First, Takagi–Sugeno (T–S) theory is employed to extend the K-Gap metric for nonlinear dynamic modeling of the suspension system. Subsequently, the Koopman operator framework is introduced to lift the system states into a high-dimensional observable space, enabling a globally linear representation of the system. Building upon Koopman-based stable kernel representation (SKR), a more accurate K-Gap residual metric is constructed. Finally, a unified fault diagnosis scheme is developed with the K-Gap metric as the core indicator, and the two approaches are experimentally compared in terms of their performance in detecting and isolating multiplicative faults. The experimental results demonstrate that the Koopman-based method significantly outperforms the T–S fuzzy model in terms of residual separability, fault classification accuracy, and diagnostic stability, confirming its effectiveness and superiority for fault diagnosis in complex nonlinear systems. Full article

In this paper, the issue of robust sensor fault estimation for Takagi–Sugeno (T–S) fuzzy systems with Caputo–Hadamard fractional-order dynamics subject to monotone nonlinearities is addressed. An adaptive observer is designed for the joint estimation of the system state and a globally constant sensor bias fault. The Caputo–Hadamard operator is used to handle logarithmic memory effects, and the T–S fuzzy representation is used for multi-regime nonlinear dynamics through a convex interpolation structure. Sufficient linear matrix inequality (LMI) conditions are obtained to ensure generalized Mittag–Leffler stability of the augmented estimation error system under a constant-fault assumption, by combining a sector inequality for strongly monotone nonlinearities with a fractional Lyapunov approach. The stability conditions are directly posed in the decision variables and the observer gains are recovered through a standard change of variables. To broaden the engineering applicability of the result, a finite-horizon practical Mittag–Leffler stability theorem is also derived for absolutely-continuous time-varying sensor faults whose Caputo–Hadamard derivative is bounded on the operating horizon $[t_0,T]$, in which the augmented estimation error remains in a residual ball whose radius is proportional to that bound. An alternative design, called a polynomial gain-scheduled observer, is also developed to reduce the conservatism of the constant-gain design, with observer gains given as polynomials of a measurable, fault-free scheduling vector. Quantitative root-mean-square performance metrics, LMI feasibility margins and an adaptation-gain sensitivity study are reported, and the polynomial matrix inequality is certified both by a dense grid check and by a sum-of-squares (SOS) feasibility argument so that the polynomial design is supported by a constructive certificate over the admissible scheduling set. Three numerical scenarios with fractional order $0.8$ are provided: a strict constant-bias scenario that exactly validates the LMI theorem, a bounded-derivative ramp scenario that validates the practical Mittag–Leffler theorem, and a polynomial gain-scheduled scenario that validates the polynomial observer. Full article

This paper investigates uncertainty-resilient stabilization of an inverted pendulum on a cart (IPOC) using an interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model and an LQR-based control framework. The IPOC dynamics are represented as a weighted combination of local linear subsystems, where interval firing strengths derived from upper and lower membership functions capture modeling uncertainties. An LQR state-feedback controller is designed for each subsystem, and the final control input is obtained by blending the local controllers according to the normalized firing strengths. To analyze stability, an LMI-based verification condition is established as a sufficient condition for the subsystem LQR controllers. Simulation results show that this condition is satisfied only in a limited operating region, while the closed-loop system can still remain stable even when the condition is violated, demonstrating the reduced conservatism and flexibility of the proposed approach. Furthermore, comparisons with the conventional PDC structure confirm that the proposed method provides greater design flexibility and enables a trade-off between robustness and transient-state performance. Full article

This paper investigates the problem of non-fragile dissipative filtering for discrete-time nonlinear networked systems with dynamic quantization, a dynamic event-triggered mechanism and stochastic cyber attacks. The nonlinear networked system under investigation is described by an uncertain Takagi–Sugeno (T-S) fuzzy model. In this work, a novel fuzzy-dependent dynamic event-triggered communication scheme and the dynamic quantization strategy, integrated with an online adjustment rule, are introduced to reduce the frequency and volume of data transmission, thus realizing more rational utilization of the limited communication resources. In addition, the stochastic cyber attacks are characterized by a random variable obeying the Bernoulli distribution. The core focus of this paper is to design a non-fragile filter such that the resulting filtering error system is stochastically stable and meets the prescribed dissipative filtering performance. Based on the matrix inequality decoupling technique, the design conditions of the desired filter are derived and presented in the form of linear matrix inequalities (LMIs). Finally, the effectiveness and superiority of the proposed filter design approach is verified via two simulation examples. Full article

The Takagi–Sugeno (T-S) fuzzy system is extensively applied in system identification and intelligent control due to its strong nonlinear approximation capability and model interpretability. However, traditional zero-order T-S systems encounter three critical limitations: slow convergence and susceptibility to local optima caused by random initialization, overfitting risks stemming from structural redundancy, and gradient oscillations during rule pruning when using traditional non-smooth regularizers (e.g., $L_{1/2}$). To overcome these challenges, this study proposes a novel gradient learning algorithm that integrates Fuzzy C-Means (FCM) clustering initialization with a smoothing Group Lasso regularization strategy. First, FCM data-drivenly initializes Gaussian membership centers and determines the rule quantity, optimizing the alignment between the initial network structure and underlying data distribution to accelerate training and reduce local optima traps. Second, a piecewise smoothing function is designed to approximate the Group Lasso penalty, facilitating efficient rule reduction through group sparsity constraints while completely resolving gradient oscillation issues arising from nondifferentiability. The global convergence of the proposed algorithm is rigorously established using Lagrange’s mean value theorem, Taylor expansion, and the differential mean value theorem. Comprehensive numerical experiments on nonlinear regression and classification benchmarks demonstrate substantial improvements in convergence rate, computational efficiency, and structural sparsity. Ultimately, this research delivers a theoretically sound and practically efficient framework for T-S fuzzy system optimization, significantly broadening the applicability of fuzzy neural networks in complex engineering scenarios. Full article

Adaptive cruise control (ACC) is a key component of advanced driver assistance systems, as it maintains a safe distance from preceding vehicles by regulating speed and spacing. However, vehicle dynamics, measurement uncertainty, and traffic variability pose significant challenges for conventional control methods. In this context, fuzzy logic (FL) has been widely explored for its ability to handle uncertainty and incorporate expert knowledge via linguistic rules. This article presents a systematic literature review on the application of FL in ACC systems, proposing a functional taxonomy based on the role of the fuzzy system within the control architecture. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 103 initial records were identified, of which 87 studies were included in the final analysis. Four main categories are defined: Direct Fuzzy Control/Learning-Based, Fuzzy Supervisory Decision Control, Fuzzy Adaptive Robust Control, and Fuzzy Model-Based Control. Results indicate that Direct Fuzzy Control/Learning-Based and Fuzzy Supervisory Decision Control dominate the literature, accounting for 35.6% and 28%, respectively, while Fuzzy Adaptive Robust Control and Fuzzy Model-Based Control represent 20.7% and 14.9%. Mamdani-type systems predominate (78.16%), followed by Takagi-Sugeno (T–S) systems (17.24%), while type-2 fuzzy systems remain limited (4.60%) due to higher computational complexity. Recent trends highlight growing interest in adaptive and robust FL-based strategies. Full article

This paper investigates the problem of stability analysis for Takagi–Sugeno fuzzy systems with time-varying delays. By integrating an augmented delay-dependent Lyapunov–Krasovskii functional (LKF) structure, a refined LKF based on auxiliary function-based integral inequalities, and utilizing a linear switching method, this paper proposes less conservative stability criteria that effectively enhance fuzzy membership characteristics. The proposed stability criteria are formulated in the framework of linear matrix inequalities. Through three numerical examples, the effectiveness and superiority of the proposed approach are demonstrated by achieving significantly improved maximum delay bounds compared to the existing literature. Full article

This paper investigates the reliable stabilization and $L_2-L_{\infty }$ performance control problem for discrete-time descriptor systems described by Takagi–Sugeno (T-S) fuzzy models under stochastic data dropouts and actuator faults. In view of the practical situation that system states are usually unmeasurable, a novel observer-based proportional–derivative (PD) control strategy is proposed. Different from traditional state feedback, the PD structure effectively alleviates the inherent structural constraints of descriptor systems and relaxes the conditions for system regularity and causality. By constructing a parameter-dependent Lyapunov functional and using the Schur complement lemma, sufficient conditions are derived in the form of linear matrix inequalities (LMIs) to guarantee the stochastic stability of the closed-loop system and the prescribed $L_2-L_{\infty }$ performance. The effectiveness and superiority of the proposed methodology are verified through extensive numerical simulations on two practical case studies, namely, a bio-economic system and a DC motor system. In the case of actuator faults and data dropouts the observer achieves accurate state tracking, and the peak value of the system output is strictly constrained. The research results confirm that the method has strong robustness against data dropouts and actuator faults. Full article

This paper investigates the transient stability characteristics of a DC-coupled off-grid photovoltaic hydrogen production system. A nonlinear state-space model of the system is established by integrating the photovoltaic generation unit, the energy storage unit, and the electrolyzer unit. To enhance system dynamic performance, a virtual DC machine (VDCM) control strategy is introduced for the energy storage converter. Based on the nonlinear system model, a Takagi–Sugeno (TS) fuzzy model is constructed to approximate the system dynamics, and the largest estimated domain of attraction (LEDA) is derived using Lyapunov stability theory. Simulation studies are conducted to evaluate system stability under sudden photovoltaic power fluctuations caused by environmental disturbances, and the obtained LEDA is compared with the simulated attraction domain and the power boundary derived from the Lyapunov eigenvalue method. The results show that the LEDA obtained from the TS fuzzy model can effectively estimate the stability boundary of the system, although it remains slightly conservative. Furthermore, the impacts of VDCM control parameters and electrolyzer operating states on system stability are analyzed. Simulation results demonstrate that appropriate adjustment of system parameters can enlarge the LEDA and significantly improve the transient stability of the off-grid photovoltaic hydrogen production system. Full article

The high-performance control of nonlinear industrial plants in a wide operation range requires intelligent techniques. The aim of the present research is to develop an engineering approach for adaptation of the gains of the well-mastered and widely applied linear PID controller based on an offline-trained backpropagation artificial neural network (BANN) that assesses the plant parameters for the current operation point. The controller’s gains are online-computed from the empirical relationship with the plant parameters. Robust stability and robust performance conditions are derived for the gain-scheduled BANN-PID system. Their fulfilment ensures system feasibility in an industrial environment. The approach is demonstrated for the control of temperature in a laboratory dryer for fruits. The BANN training is based on data derived and validated from experiments using the Takagi–Sugeno–Kang nonlinear plant model. Simulations show that the BANN-PID system outperforms both the gain-scheduled fuzzy logic PID control system, designed in previous research, and the PID real-time control system by reducing overshoot six times and settling time 1.8 times and improving robustness 1.3 times. Full article

This research focuses on the trajectory tracking control of a Twin Rotor MIMO System (TRMS) with time-varying sinusoidal inputs. Initial design considerations include a backstepping controller integrated with a high-gain observer (HGO) to estimate unmeasured states. While the outcomes of the simulation show good accuracy of tracking, real-time implementation shows instability and performance degradation. This divergence is attributed to the static high gains of the observer that amplify measurement noise and inject inaccurate state estimates into the controller during actual deployment. To overcome this drawback without altering the core control structure, we propose a strategy of online gain tuning based on Interval Type-2 Takagi–Sugeno (TS) fuzzy logic. The proposed mechanism dynamically adjusts the observer gain based on estimation errors to balance the trade-off between convergence speed and noise sensitivity. Experimental evaluations on the physical TRMS confirm that the fuzzy-tuned observer eliminates instability in real-time. Quantitative analysis demonstrates that the proposed method reduces the Root Mean Square Error (RMSE) by 65.6% in the Pitch axis and 92.3% in the Yaw axis compared to the fixed-gain counterpart. Full article

This paper presents a fuzzy satisfaction-based intelligent framework for early-stage multiobjective sizing of a buck DC–DC converter under uncertain operating conditions. Lightweight closed-form estimators are used to evaluate inductor current ripple, output voltage ripple, and efficiency, including an explicit decomposition of ripple into capacitive and ESR-induced components to distinguish capacitance-dominated and ESR-dominated regimes. Engineering targets for ripple, efficiency, and passive size/cost pressure are mapped to reproducible piecewise membership functions and aggregated into a bounded overall satisfaction score using a weighted geometric operator; alternative non-compensatory and OWA-type aggregators are considered for sensitivity analysis. The resulting nonconvex design problem is solved via a compact two-stage derivative-free strategy that combines global screening with an interpretable Takagi–Sugeno (TSK) rule-based refinement layer, which generates bounded, physics-consistent updates of the design variables and supports rapid feasibility restoration followed by preference-driven tuning. Uncertainty in operating conditions and parameter drift is addressed through scenario evaluation and worst-case or average-case aggregation of satisfaction, linking the fuzzy decision objective to robust scenario design. Numerical studies for a 24 ± 4 V to 12 V converter illustrate regime-dependent adaptation: in low-ESR conditions, ripple improvement is driven mainly by capacitance/frequency adjustments, while in high-ESR conditions, the rule base shifts corrections toward inductor and frequency choices that reduce ESR-dominated ripple. Full article

This study focuses on the zonotope-based set-membership estimation issue for switched Takagi–Sugeno (T-S) fuzzy systems with application to tunnel diode circuits. Given the practical importance of tunnel diodes in radio-frequency, microwave, and high-speed electronic systems, we first model the tunnel diode circuit as a switched T-S fuzzy system to characterize its inherent dynamics. To address the state estimation issue, we propose a zonotopic set-membership estimation framework for the system under mode-dependent average dwell-time (MDADT) switching, which enables tighter state bounding while ensuring $H_{\infty }$ robustness. A mode-dependent observer is designed to attenuate the effects of external disturbances and measurement noise, and the stability of the estimation error system is analyzed based on an appropriate Lyapunov function. Numerical simulations are conducted and the corresponding results show that the estimated boundary can accurately encompass the true state of the system, and the volume of the estimated set is reduced by approximately 28.99% compared with the interval observer method, thus demonstrating the effectiveness and potential of the proposed approach. Full article

This work presents the real-world deployment of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for intelligent energy control in resource-constrained IoT devices. The proposed system employs a first-order Takagi–Sugeno fuzzy model with three Gaussian membership functions per input: ambient temperature, light intensity, and battery voltage. The model was trained offline using augmented environmental datasets and subsequently translated into optimized embedded C code for execution on an ESP32 microcontroller. The controller dynamically adjusts the node’s deep sleep duration according to environmental conditions, enabling adaptive behavior based solely on local environmental conditions without requiring external connectivity. A 10-day field deployment compared the ANFIS controller with conventional fixed and rule-based strategies. Results show that the ANFIS-based strategy reduced energy consumption by 31.1% relative to the fixed approach while maintaining accurate adaptation to environmental conditions (RMSE = 9.6 s). The inference process required less than 2.5 ms and used under 30 KB of RAM, confirming the feasibility of real-time fuzzy inference on resource-constrained embedded platforms. Full article