Takagi-Sugeno Research Articles

Privacy-Preserving Modeling of Trajectory Data: Secure Sharing Solutions for Trajectory Data Based on Granular Computing

Trajectory data are embedded within driving paths, GPS positioning systems, and mobile signaling information. A vast amount of trajectory data play a crucial role in the development of smart cities. However, these trajectory data contain a significant amount of sensitive user information, which poses a substantial threat to personal privacy. In this work, we have constructed an internal secure information granule model based on differential privacy to ensure the secure sharing and analysis of trajectory data. This model deeply integrates granular computing with differential privacy, addressing the issue of privacy leakage during the sharing of trajectory data. We introduce the Laplace mechanism during the granulation of information granules to ensure data security, and the flexibility at the granularity level provides a solid foundation for subsequent data analysis. Meanwhile, this work demonstrates the practical applications of the solution for the secure sharing of trajectory data. It integrates trajectory data with economic data using the Takagi–Sugeno fuzzy rule model to fit and predict regional economies, thereby verifying the feasibility of the granular computing model based on differential privacy and ensuring the privacy and security of users’ trajectory information. The experimental results show that the information granule model based on differential privacy can more effectively enable data analysis.

T–S Fuzzy Observer-Based Output Feedback Lateral Control of UGVs Using a Disturbance Observer

This paper introduces a novel observer-based fuzzy tracking controller that integrates disturbance estimation to improve state estimation and path tracking in the lateral control systems of Unmanned Ground Vehicles (UGVs). The design of the controller is based on linear matrix inequality (LMI) conditions derived from a Takagi–Sugeno fuzzy model and a relaxation technique that incorporates additional null terms. The state observer is developed to estimate both the vehicle’s state and external disturbances, such as road curvature. By incorporating the disturbance observer, the proposed approach effectively mitigates performance degradation caused by discrepancies between the system and observer dynamics. The simulation results, conducted in MATLAB and a commercial autonomous driving simulator, demonstrate that the proposed control method substantially enhances state estimation accuracy and improves the robustness of path tracking under varying conditions.

Intelligent Control of an Experimental Small-Scale Wind Turbine

Nowadays, wind turbines are one of the most popular devices for producing clean and renewable electric energy. The rotor blades catch the wind’s kinetic energy to produce rotational energy from the turbine and electric energy from the generator. In small-scale wind turbines, there are several methods to operate the blades to obtain the desired speed of rotation and power outputs. These methods include passive stall, active stall, and pitch control. Pitch control sets the angular position of the blades to face the wind to achieve a predefined relationship between turbine speed or power and wind velocity. Typically, conventional Proportional Integral (PI) controllers are used to set the angular position of the rotor blades or pitch angle. Nevertheless, the quality of speed or power regulation may vary substantially. This study introduces a rotor speed controller for a pitch-controlled small-scale wind turbine prototype based on fuzzy logic concepts. The basics of fuzzy systems required to implement this kind of controller are presented in detail to counteract the lack of such material in the technical literature. The knowledge base of the fuzzy speed controller is composed of Takagi–Sugeno–Kang (TSK) fuzzy inference rules that implement a dedicated PI controller for any desired interval of wind velocities. Each wind velocity interval is defined with a fuzzy set. Simulation experiments show that the TSK fuzzy PI speed controller can outperform the conventional PI controller in the speed and accuracy of response, stability, and robustness over the whole range of operation of the wind turbine prototype.

Enhancing Electric Vehicle Propulsion: PMSM Motor Speed Control via a Fuzzy PI Controller Developed Using a Line-Integral Lyapunov Fuzzy Function

This paper suggests a robust control strategy to enhance the stability and ensure optimal tracking of reference speed for electric vehicles (EVs) equipped with Permanent Magnet Synchronous Motors (PMSM). The strategy involves designing a specific Proportional-Integral (PI) controller using the Takagi-Sugeno (TS) fuzzy model and employing the Linear Integral Lyapunov Function (LILF). Additionally, an H∞ approach is integrated to effectively manage external disturbances such as load torque. The PI controller's gains are determined using the Linear Matrix Inequality (LMI) tool, ensuring efficient and reliable performance under varying operating conditions. Simulation studies confirm the effectiveness of the proposed control strategy and highlighting its capability to achieve robust speed regulation and disturbance rejection in EV propulsion systems

Attack Reconstruction and Attack-Resilient Consensus Control for Fuzzy Markovian Jump Multi-Agent Systems

Driven by the rapid development of modern industrial applications, multi-agent systems (MASs), integrating computational and physical resources, have become increasingly important in recent years. However, the performance of MASs can be easily compromised by malicious false data injection attacks (FDIAs) due to the inherent vulnerability of the cyber layer. This work focuses on an event-triggered framework for secure reconstruction and consensus control in MASs subject to both sensor and actuator attacks. First, we introduce a class of Takagi–Sugeno fuzzy multi-agent systems that relax the traditional Lipschitz condition and incorporate realistic system dynamics by considering parameter variations governed by Markovian jump principles. Second, an adaptive fuzzy estimator is developed for the simultaneous reconstruction of states and attacks in MASs. The derived estimates are utilized to design an attack-resilient consensus control strategy that compensates for the effects of FDIAs on the closed-loop consensus error dynamics. Meanwhile, the sufficient conditions for the convergence of both estimation and consensus errors are presented and rigorously proved. Finally, evaluation results on an experimental platform through multiple truck-trailer systems are provided to demonstrate the effectiveness and performance of the proposed approach.

Observer‐based PID control for fuzzy dynamic systems with memory triggering protocol

AbstractThis paper addresses the issue of observer‐based proportional–integral–derivative control for Takagi–Sugeno fuzzy dynamic systems using a memory triggering protocol. To tackle the challenges of managing nonlinear systems, the Takagi–Sugeno fuzzy approach is employed to approximate these systems with a series of local linear models. A novel memory event‐triggering mechanism is introduced, utilizing a computer's storage unit to store historical data and adjust the triggering threshold based on this data, thereby reducing unnecessary network resource usage. Given that the system state can be affected by external disturbances and become unpredictable, an observer‐based proportional–integral–derivative controller is designed to manage these uncertainties. Finally, a car‐damper‐spring model is presented to validate the proposed methodology.

Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making

In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article introduces a scenario network-based approach for managing cybersecurity projects, utilizing fuzzy linguistic models and a Takagi–Sugeno–Kanga fuzzy neural network. Drawing upon L. Zadeh’s theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. Fuzzy production rules are employed for decision-making, while the Mamdani fuzzy inference algorithm enhances interpretability. This approach enables multi-scenario planning and adaptability across multi-stage cybersecurity projects. Preliminary results from a research prototype of an intelligent expert system—designed to analyze project stages and adaptively construct project trajectories—suggest the proposed approach is effective. In computational experiments, the use of fuzzy procedures resulted in an over 25% reduction in errors compared to traditional methods, particularly in adjusting project scenarios from pessimistic to baseline projections. While promising, this approach requires further testing across diverse cybersecurity contexts. Future studies will aim to refine scenario adaptation and optimize system response in high-risk project environments.

State and Fault Interval Estimation for Discrete-Time Takagi–Sugeno Fuzzy Systems via Intermediate Observer Base on Zonotopic Analysis

State and Fault Interval Estimation for Discrete-Time Takagi–Sugeno Fuzzy Systems via Intermediate Observer Base on Zonotopic Analysis

A Closed-Form Solution to Observer Design Problem for Ostensible Metzler Takagi-Sugeno Systems

This paper addresses the state estimation problems related to the generalized fuzzy observer design for ostensible Metzler Takagi-Sugeno (T-S) systems. Attention is focused on design constraints for the concept of diagonal stabilization and positivity of observer gain matrices. On the basis of some new interpretations, the parameterizations of ostensible Metzler T-S fuzzy systems is presented, which opens the way to the solution of the design problem using only the principle of linear matrix inequalities. The same approach is intended to ensure the stability of the dynamics of the estimation error. The presented method extends and generalizes the results that have been presented in the literature so far.

Attack-Dependent Adaptive Event-Triggered Security Fuzzy Control for Nonlinear Networked Cascade Control Systems Under Deception Attacks

This article investigates the issue of H∞ security output feedback control for a nonlinear networked cascade control system with deception attacks. First, to further reduce the amount of communication data, reasonably schedule network resources, and alleviate the impact of multi-channel deception attacks, an attack-dependent adaptive event-triggered mechanism is introduced into the primary network channel, and its adaptive triggered threshold can be adjusted according to the random attack probability. Secondly, the output dynamic quantization of the secondary network channel is considered. Then, a novel security cascade output feedback controller design framework based on the Takagi–Sugeno (T-S) fuzzy networked cascade control system under deception attacks is established. In addition, by introducing the Lyapunov–Krasovskii stability theory, the design conditions of the controller are given. Finally, the effectiveness and superiority of the proposed design strategies are verified by two simulation examples of power plant boiler–turbine system and power plant boiler power generation control system.

H∞$$ {\boldsymbol{H}}_{\mathbf{\infty}} $$ Control With Event‐Triggered Mechanism for T‐S Fuzzy System Under Multiple Cyber‐Attacks

ABSTRACTThe problem of event‐triggered control for networked Takagi–Sugeno (T‐S) fuzzy system under aperiodic DoS attacks and deception attacks is investigated. First, an event generator is introduced in the Sensor‐to‐Controller channel to determine the transmission of data. At the same time, the Sensor‐to‐Controller channel is assumed to be subjected to deception attacks that are randomly distributed but not Bernoulli distributed. Next, the impact of aperiodic DoS attacks on the Controller‐to‐Actuator channel is further considered, and the DoS attack behavior is described in terms of attack period and frequency. The article designs an adaptive resilience event‐triggered mechanism (ARETM), which is aimed at circumventing the ineffective data updating in the “active” phase of the DoS attacks, thereby realizing the effective saving of communication resources and mitigating the adverse effects of DoS attacks. Then, the switched fuzzy system is established to cope with the different states of the DoS attackers. Using the piecewise Lyapunov function, a design method for the controller gains and the ARETM matrix is obtained, which allows the system to be stabilizable and obtain performance under the control action. Finally, the effectiveness of the ARETM‐based control strategy is confirmed by simulation experiment.

Quasi-static modeling of a full-channel effective magnetorheological damper with shunt and bending magnetic circuit

Magnetorheological dampers (MRD) are widely used in vibration control due to their rapid response and adjustable damping forces. However, conventional MRD designs with closed rectangular magnetic circuits suffer from limited effective working lengths and inadequate damping force for vehicle suspensions. To address this, a novel full-channel effective MRD with shunt and bending magnetic circuit (FEMRD_SB) is proposed. This design increases the effective working length of the damping channel by incorporating a bending motion in the magnetic circuit. Magnetic circuit shunting is integrated to mitigate magnetic leakage during bending, improving energy utilization efficiency. To better meet the practical needs of vehicle suspension, a quasi-static mathematical model aiming to explain the mechanical properties of the damper is investigated. Traditional models often overlook the uneven distribution of magnetic fields, which leads to poor modeling accuracy. To improve the precision of the quasi-static model, a multiphysical field coupling quasi-static modeling method is proposed. This method, utilizing the Takagi–Sugeno fuzzy neural network establishes the relationship between magnetic field and displacement, facilitating the integration of magnetic, flow, and solid fields for more precise modeling outcomes. Simulations and experiments validate the effectiveness of the FEMRD_SB and the quasi-static model. The real-vehicle experiments demonstrate that the FEMRD_SB effectively suppresses the sprung mass acceleration of a vehicle, improving the vehicle’s ride comfort.

New delay‐dependent finite‐time stabilization of Takagi–Sugeno fuzzy approaches for delayed nonlinear systems

AbstractIn this paper, the finite‐time stability and stabilization of nonlinear systems with delays is studied, via a Takagi–Sugeno approach. By using a novel Lyapunov–Krasovskii functional and introducing some fuzzy free‐weighting matrices, sufficient conditions are derived, for bounded and differentiable time‐varying delays in terms of an upper bound of the delay derivatives. Then, we achieve closed‐loop stabilization in finite time through an efficient parallel distributed compensation design. The sufficient conditions are formulated as linear matrix inequalities to achieve the desired performance. Finally, the proposed methodology is applied to various case studies, highlighting its significance.

Stability of high-order nonlinear Takagi–Sugeno fuzzy impulsive delayed coupled systems

In this study, we consider the stability of high-order nonlinear Takagi–Sugeno fuzzy impulsive delayed coupled systems (TSFICSs). It should be noted that the linear growth condition is removed in the investigation of high-order nonlinear TSFICSs, which weakens the conditions compared with previous studies of impulsive systems. In addition, the existing research methods do not work for high-order nonlinear TSFICSs, so novel impulsive differential inequalities with high-order terms are established to investigate the stabilization of high-order nonlinear TSFICSs and develop the classic impulsive differential inequalities for high-order nonlinear situations. Next, sufficient conditions for the stability are obtained based on the new impulsive differential inequalities together with graph theory and the Lyapunov method. Finally, the theoretical results are applied to modified impulsive delayed coupled Fitzhugh–Nagumo models, and numerical simulations are provided to illustrate the practical value of the derived results.

Fractional Caputo Operator and Takagi–Sugeno Fuzzy Modeling to Diabetes Analysis

Diabetes is becoming more and more dangerous, and the effects continue to grow due to the population’s ignorance of the seriousness of this phenomenon. The studies that have been carried out have not been able to follow the phenomenon more precisely, which has led to the use of the fractional derivative tool, which has a very great capability to study real problems and phenomena but is somewhat limited on nonlinear models. In this work, we will develop a new fractional derivative model of a diabetic population, the Takagi–Sugeno fractional fuzzy model, which will enable us to study the phenomenon with these nonlinear terms in order to obtain greater precision in the results. We will study the existence and uniqueness of the solution using the Lipschizian theorem and then turn to the new fuzzy model, which leads us to four dynamical systems. The interpretation results show the quality of fuzzy membership in tracking the malleable phenomena of nonlinear terms existing in the system.

New insights on dissipative control technique for Takagi–Sugeno fuzzy system with variable time-delays and random packet dropouts

New insights on dissipative control technique for Takagi–Sugeno fuzzy system with variable time-delays and random packet dropouts

A Fractional-Order Model Predictive Control Strategy with Takagi–Sugeno Fuzzy Optimization for Vehicle Active Suspension System

Aiming at the problem of system controller performance failure caused by improperly setting the value of each weighting coefficient of the model predictive control (MPC), a fractional-order MPC strategy with Takagi–Sugeno fuzzy optimization (T–SFO MPC) is proposed for a vehicle active suspension system. Firstly, the fractional-order model predictive control framework for active suspension systems is designed based on a 1/4 vehicle model. Then, we analyze the influence of different weighting coefficients on the suspension performance and introduce the Takagi–Sugeno fuzzy optimization theory to adaptively adjust the weighting coefficients of the fractional-order MPC controller. Finally, the system responses of the T–SFO MPC, traditional MPC, linear quadratic regulator (LQR), and passive suspension control are numerically analyzed under various road conditions. Simulation results show that suspension response with the T–SFO MPC is significantly improved compared with passive suspension control, traditional MPC control, and LQR control, and the weight coefficients of the T–SFO MPC can be adaptively adjusted according to the dynamic changes of suspension response. Compared with passive suspension, the root mean square (RMS) value of the vertical acceleration of the T–SFO MPC under various roads decreased by a maximum of 37.97%, and the RMS value of suspension dynamic deflection and tire dynamic load decreased by a maximum of 32.94% and 37.8%, respectively. These results validate that the proposed control method can achieve coordinated optimization of vehicle comfort and handling stability.

Fractional impulsive controller design of fractional-order fuzzy systems with average dwell-time strategy and its application to wind energy systems

In this study, the issue of adaptive impulsive control of a permanent magnet synchronous generator (PMSG)-based wind energy system (WES) with an average dwell-time strategy is investigated using a Takagi–Sugeno (T–S) fuzzy model described by fractional-order differential equations. A T–S fuzzy model is employed to describe the nonlinear fractional-order PMSG (FOPMSG) model, an average dwell time, an average impulsive condition, a Lyapunov function, and a less conservative algebraic inequality criterion that guarantees stabilization for the considered nonlinear system. First, the mathematical model of the FOPMSG in the n−m reference frame is transformed into a dimensionless chaotic system through affine transformation and time scale transformation. Then, the stabilization problem of the nonlinear chaotic FOPMSG model is considered to validate the proposed sufficient conditions, leading to the derivation of a new stability criterion for the FOPMSG model, instead of addressing the general problem to validate the proposed result. Subsequently, the desired control gains can be obtained to ensure the stabilization of the addressed closed-loop system. By combining adaptive and impulsive control, the model under consideration can be stabilized for any target dynamics. Finally, we demonstrate the efficiency and feasibility of the suggested approach through numerical simulations and comparative results.

Design of extended dissipative-based composite anti-disturbance reliable tracking control for stochastic Takagi–Sugeno fuzzy systems

This study focuses on examining the issues of extended dissipative-based composite anti-disturbance reliable tracking control for stochastic Takagi–Sugeno fuzzy systems afflicted by sensor faults and multiple disturbances. Explicitly, the multiple disturbances include the autonomous Wiener process and non random noises with partly known information that are generated by an exogenous plant. To address the difficulty of estimating the non random disturbance while considering the partially known information, the study incorporates a fuzzy stochastic disturbance observer. Furthermore, to solve the output tracking issue of the system under consideration, a modified repetitive tracking control is implemented. Then, combining the methodologies of disturbance observer-based control technique with modified repetitive control, a composite anti-disturbance reliable tracking control strategy is developed for achieving the intended tracking goal despite the multiple disturbances and sensor faults encountered. Additionally, the proposed approach involves extended dissipativity scheme, which includes H ∞ , passivity, dissipativity, and L 2 − L ∞ performances in a unified framework. In particular, by employing Lyapunov–Krasovskii functional approach, the essential stability criteria for the proposed closed-loop system are articulated in the form of linear matrix inequalities. Additionally, numerical illustrations are presented to exemplify the application and efficiency of the developed control protocol.

Finite Time Stability Analysis and Feedback Control for Takagi–Sugeno Fuzzy Time Delay Fractional-Order Systems

This study treats the problem of Finite Time Stability Analysis (FTSA) and Finite Time Feedback Control (FTFC), using a Linear Matrix Inequalities Approach (LMIA). It specifically focuses on Takagi–Sugeno fuzzy Time Delay Fractional-Order Systems (TDFOS) that include nonlinear perturbations and interval Time Varying Delays (ITVDs). We consider the case of the Caputo Tempered Fractional Derivative (CTFD), which generalizes the Caputo Fractional Derivative (CFD). Two main results are presented: a two-step procedure is provided, followed by a more relaxed single-step procedure. Two examples are presented to show the reduction in conservatism achieved by the proposed methods. The first is a numerical example, while the second involves the FTFC of an inverted pendulum, which exhibits both symmetrical dynamics for small angular displacements and asymmetrical dynamics for larger deviations.