Takagi-Sugeno Fuzzy Model Research Articles

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.

Takagi–Sugeno Fuzzy Parallel Distributed Compensation Control for Low-Frequency Oscillation Suppression in Wind Energy-Penetrated Power Systems

In this paper, a Takagi–Sugeno fuzzy parallel distributed compensation control (TS-PDCC) is proposed for low-frequency oscillation (LFO) suppression in wind energy-penetrated power systems. Firstly, the fuzzy C-mean algorithm (FCMA) is applied to cluster the daily average wind speed of the wind farm, and the obtained wind speed clustering center is used as the premise variable of TS-PDCC, which increases the freedom of parameter setting of the TS fuzzy model and is closer to the actual working environment. Secondly, based on the TS fuzzy model, the TS-PDCC is designed to adjust the active power output of the wind turbine for LFO suppression. To facilitate the computation of controller parameters, the stability conditions are transformed into a set of Linear Matrix Inequalities (LMIs) via the Schur complement. Subsequently, a Lyapunov function is designed to verify the stability of the wind energy-penetrated power system and obtain the parameter ranges. Simulation cases are conducted to verify the validity and superior performance of the proposed TS-PDCC under different operating conditions.

Integrated Fault-Tolerant Control Design With Sampled-Output Measurements for Interval Type-2 Takagi-Sugeno Fuzzy Systems.

This article is concerned with the integrated design of fault estimation (FE) and fault-tolerant control (FTC) for uncertain nonlinear systems suffering from actuator faults and external disturbance. The uncertain nonlinear systems are characterized as the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model, and IT2 membership functions are employed to effectively handle uncertainties. A fuzzy observer, utilizing only sampled-output measurements, is applied to simultaneously estimate actuator faults and system states. Based on the estimation, the fault-tolerant controller is designed to ensure the system stability under a predefined H∞ performance. The sampling behavior complicates the system dynamics and makes the integrated FTC design more challenging. To confront this issue, the discontinuous Lyapunov functional technique is exploited to enhance stability results by considering the sampling characteristic, upon which FE and FTC units are co-designed in the linear matrix inequality (LMI) framework. To further relax stability criteria, the analysis process incorporates the bound information of membership functions through the membership-function-dependent (MFD) method. Additionally, the relationship of mismatched premise variables resulting from the sampling scheme is also taken into account. Moreover, considering the imperfect premise matching (IPM) framework, the proposed fault-tolerant controller provides greater flexibility in selecting the shapes of membership functions and number of fuzzy rules that can vary from the counterpart of the fuzzy system. Finally, the efficacy of the proposed FTC technique is validated through a detailed numerical example.

Relaxed TS Fuzzy Model Transformation to Improve the Approximation Accuracy/Complexity Tradeoff and Relax the Computation Complexity

Relaxed TS Fuzzy Model Transformation to Improve the Approximation Accuracy/Complexity Tradeoff and Relax the Computation Complexity

A novel approach to decentralized sampled‐data fuzzy observer design for nonlinear interconnected systems

AbstractThis paper presents the decentralized sampled‐data fuzzy observer design technique for nonlinear interconnected systems based on the Takagi–Sugeno fuzzy model. The interconnected system is considered that the subsystems include uncertain interconnections and are not needed to satisfy stable conditions. Based on the continuous‐time Lyapunov functional approach, the observer design technique is proposed to minimize the interconnection bound to attenuation degree ratio and formulated into the linear matrix inequality formats. Finally, by simulation example, it shows the validity and superiority of the proposed observer design technique.

Quantized based dynamic-output-feedback guaranteed-cost control of interval-type-2 Takagi-Sugeno fuzzy systems under DoS attacks

This article investigates the issue of quantized guaranteed-cost dynamic-output-feedback control for uncertain-nonlinear NCSs with DoS attacks. Firstly, the interval-type-2 Takagi-Sugeno fuzzy model is built to deal with the nonlinear NCSs. Secondly, to reduce the burden on communication resources, the dynamic quantization is introduced from measurement-output and control-input. Considering the existence of DoS attacks in network channel, the DoS attacks model is constructed by binary-Markov-chain. This paper aims to obtain the controller and quantizers with DoS attacks. Some sufficient conditions of uncertain-nonlinear NCSs are received to ensure the stochastic stability and guaranteed-cost performance. Finally, an example is simulated to prove the advantage of proposed method.

Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system

Combined Energy and Attitude Control System (CEACS) reduces the size and mass budgets of typical satellites and consequently, increases their payload capacity. CEACS uses flywheels for a dual purpose, i.e., as both energy storage and attitude control device. This maiden work attempts to introduce a novel Deep-Learning capability of the fuzzy-Model Predictive Control (FMPC) controller for CEACS. The design approach for the fuzzy-MPC controller uses the Takagi-Sugeno (T-S) fuzzy model of satellite attitudes and computes the control torque through a parallel distribution compensation (PDC) approach. However, the MPC controller offers a high computational burden, and it becomes a significant problem for smaller satellites having limited computational power. Therefore, in this research work, a novel Deep-Learning-based fuzzy-MPC controller (D-FMPC) is designed for the CEACS attitude regulation subject to higher initial angles, actuator constraints, parametric uncertainties, and external disturbance torques. Here, the deep-layer neural network is trained offline with the MPC controller data to replicate the FMPC controller, thus ensuring its controllability. Numerical results validate that the D-FMPC controller successfully mimics the FMPC controller and produces the desired pointing accuracy effectively with smooth transient response and without violating the attitude control actuator constraints. The results also validate that the D-FMPC controller offers significantly reduced computational burden than the FMPC controller. Therefore, the novel Deep-Learning solution provides a feasible platform for applying more complicated and sophisticated attitude control techniques for the CEACS attitude regulation in small satellites as an example.

Takagi-Sugeno fuzzy sampled-data observer design for nonlinear permanent magnet synchronous generator-based wind turbine systems using auxiliary function-based integral inequality technique

This study focuses on designing the fuzzy sampled-data observer (FSDO) scheme for nonlinear permanent magnet synchronous generator (PMSG)-based wind turbine systems (WTS). The primary objective of this problem is to solve an unmeasurable state problem under the mismatched premise variables conditions among system, observer, and controller, respectively. To do this, firstly, the proposed wind turbine model is described in a Takagi-Sugeno fuzzy model due to the complex nonlinearities in PMSG-based WTS. Next, based on the measured output signals and sampled estimated states, the FSDO is designed to understand some unmeasurable state variables of the studied system. Then, a novel auxiliary function-based integral inequality (AFBII) is presented to approximate the integral quadratic terms related to sampling information. Thanks to the proposed AFBII technique and the introduction of slack variables, several new and less conservative stability requirements are derived in the formulations of linear matrix inequalities (LMIs) using the looped Lyapunov function. Finally, the simulation studies for the considered wind turbine model are validated numerically, and then some comparative results are provided to show the superiority and applicability of the theoretical observations.

Internal Model Control Design for Nonlinear Systems Based on Inverse Dynamic Takagi–Sugeno Fuzzy Model

Internal Model Control Design for Nonlinear Systems Based on Inverse Dynamic Takagi–Sugeno Fuzzy Model

A switching approach to repetitive control for Takagi-Sugeno fuzzy systems

Repetitive control has learning properties and exhibits high accuracy in periodic control but struggles with nonlinearities and disturbances. To address this issue, the study presents a composite method of repetitive control and equivalent input disturbance based on the Takagi-Sugeno fuzzy model. The control structure takes into account both the continuous-discrete two-dimensional characteristics of the repetitive control and the membership function of the fuzzy system. As the first component of the control configuration, a repetitive controller is adopted for the high-precision tracking of periodic references. Then, an equivalent-input-disturbance estimator is used to compensate for exogenous disturbances. The closed-loop system is stabilized using the state feedback and the state observer. To ensure stability, membership function-dependent Lyapunov candidates are used to derive a less conservative stability condition. Consequently, all gains in the controller switch with the derivative sign of the premise variables. Finally, the developed approach is validated through comparisons with typical methods, demonstrating its effectiveness and advantages.

Human Behavior Learning for a Class of Nonlinear Human-in-the-Loop Systems via Takagi-Sugeno Fuzzy Model

Human Behavior Learning for a Class of Nonlinear Human-in-the-Loop Systems via Takagi-Sugeno Fuzzy Model

Experimental Evaluation of a Takagi–Sugeno Fuzzy Controller for an EV3 Ballbot System

In this paper, experimental results about the performance of a Takagi–Sugeno Fuzzy Controller (TSFC) for an EV3 Ballbot Robotic System (EV3BRS) are reported. The physical configuration of the EV3BRS has the form of an inverted pendulum mounted on a ball. The EV3BRS is an underactuated robotic system with four outputs and two control torques. In this work, following the Takagi–Sugeno (TS) fuzzy control design methodology, the Parallel Distributed Compensation (PDC) approach is used in the design of the TSFC. The EV3BRS’s TS Fuzzy Model (TSFM) design comes from linearization of the nonlinear model around two operation points near the upright position of EV3BRS’s body. The Linear Matrix Inequality (LMI) approach was used to obtain the feedback gains for every local linear controller, guaranteeing, via a conservative stability condition, the global asymptotic stability of the overall fuzzy control system. The main goal of the control task consists of maintaining the EV3BRS’s body at its upright position. Measurement and control data from and to the EV3BRS are transferred via telecontrol and telemetry. The appropriate performance of the controller design is corroborated via experimentation.

H∞ fault detection and control of Takagi–Sugeno continuous-time conic-type nonlinear systems

In this study, control and fault detection are examined for Takagi–Sugeno (T–S) fuzzy continuous-time systems with conic-type nonlinearity. In a conic-type dynamic system, Takagi–Sugeno fuzzy models are applied. A conic-type non-linearity must lie in an n-dimensional hyper-sphere with an uncertain centre to satisfy the restrictive condition. A combined error system of estimation and control error is obtained by combining the observations and the controller. Our main goal is to design an observer and controller for fault detection that minimizes the impact of unknown inputs and faults on the system in terms of the H ∞ performance. The Lyapunov function is used to construct the residual signal using an observer and a controller. Using Linear Matrix Inequalities (LMIs), we can express the performance conditions for H ∞ . The scheme's effectiveness is demonstrated with numerical examples.

Decentralized and performance-constrained state-estimated fuzzy control for nonlinear differential-algebraic interconnected systems

This paper introduces a decentralized and mixed performance-constrained state-estimated fuzzy control strategy designed for nonlinear differential-algebraic interconnected systems. Initially, the approach employs a Takagi-Sugeno (T-S) fuzzy model to approximate these complex systems. To address potential limitations within the interconnected systems, a Proportional-plus-Derivative (PD) state control method is integrated into the proposed control scheme. Additionally, an observer is constructed to estimate system states, enhancing the feasibility of the control method. System stability analysis is conducted using the free-weighing Lyapunov function approach, allowing designers to customize the weight matrix for desired control performance and facilitate controller design. Subsequently, a fuzzy controller is developed to ensure stability and meet both Passive-H∞ Performance (PHP) and H2 Control Performance (HCP) criteria. Finally, the effectiveness of the proposed design approach is validated through simulation results.

Decentralized Fuzzy Fault Estimation Observer Design for Discrete-Time Nonlinear Interconnected Systems

In this paper, a fault estimation technique is proposed for discrete-time nonlinear interconnected systems with uncertain interconnections. To achieve the fault estimation, the decentralized fuzzy observer is adopted based on the Takagi–Sugeno fuzzy model. Based on the estimation error model with the subsystems of the interconnected system and its decentralized fuzzy observer, the fault estimation condition with H∞ performance is presented. The main idea of this paper is that a novel inequality condition for H∞ performance is used, and the sufficient condition is presented to guarantee the good fault estimation performance. Also, the decentralized fuzzy observer design condition for the fault estimation is converted into linear matrix inequality formats. Finally, a simulation example is provided, and the effectiveness of the proposed fault estimation technique is verified by comparison of the fault estimation performance.

Sliding Mode Fuzzy Control of Stochastic Nonlinear Systems Under Cyber-Attacks.

In this article, the problem of integral sliding mode control (ISMC) for a class of nonlinear systems with stochastic characteristics under cyber-attack is investigated. The control system and the cyber-attack are modeled as an Itô-type stochastic differential equation. The stochastic nonlinear systems are approached by the Takagi-Sugeno fuzzy model. A dynamic ISMC scheme is applied and the states and control input are analyzed within a universal dynamic model. It is demonstrated that trajectory of the system can be confined to the integral sliding surface within finite time, and the stability of closed-loop system under cyber-attack will be guaranteed by using a set of linear matrix inequalities. Following a standard procedure of universal fuzzy ISMC, it is shown that all signals in the closed-loop system will be guaranteed bounded, and the states are asymptotic stochastic stable if some conditions are met. An inverted pendulum is applied to show the effectiveness of our control scheme.

Adaptive Optimal Terminal Sliding Mode Control for T-S Fuzzy-Based Nonlinear Systems

This study utilizes the Takagi–Sugeno fuzzy model to represent a subset of nonlinear systems and presents an innovative adaptive approach for optimal dynamic terminal sliding mode control (TSMC). The systems under consideration encompass bounded uncertainties in parameters and actuators, as well as susceptibility to external disturbances. Performance evaluation entails the design of an adaptive terminal sliding surface through a two-step process. Initially, a state feedback gain and controller are developed using Linear Matrix Inequality (LMI) techniques, grounded on H2-performance and partial eigenstructure assignment. Dynamic sliding gain is subsequently attained via convex optimization, leveraging the derived state feedback gain and the designed terminal sliding mode (TSM) controller. This approach diverges from conventional methods by incorporating control effort and estimating actuator uncertainty bounds, while also addressing sliding surface and TSM controller design intricacies. The TSM controller is redefined into a strict feedback form, rendering it suitable for addressing output-tracking challenges in nonlinear systems. Comparative simulations validate the effectiveness of the proposed TSM controller, emphasizing its practical applicability.

A flexible and deep peak shaving scheme for combined heat and power plant under full operating conditions

Improving the flexible and deep peak shaving capacity of combined heat and power (CHP) plant under full operating conditions to facilitate renewable energy consumption is the main choice of novel power system. Accordingly, a dry/wet state automatic conversion control scheme fuses the precise mechanism structure, reinforcement learning and multi-objective model predictive control (MPC) algorithms is designed to promote the deep peak shaving ability of CHP plant in this paper. Firstly, the detailed mechanism models of once-through boiler at dry and wet state are presented by analyzing the dynamic characteristics of steam-water flow. Secondly, the large-scale unknown parameters in mechanism models are identified via the Takagi-Sugeno fuzzy modeling, reinforcement learning algorithms and actual dry/wet state conversion data. Thanks to the proposed hybrid modeling strategy, the high-precision boiler-steam unit models at dry and wet state are rapid obtained. Then, to meet different peak shaving demands, the multi-objective MPC algorithm is respectively constructed under dry and wet state with the comprehensive consideration of operational constraints, load tracking error, algorithm stability, power generation cost, CO2 and NOx emission costs. Aiming at maximizing the peak shaving efficiency and flexible operation ability of plant, a dry/wet automatic control scheme combined the designed multi-objective MPC algorithms and identified dry/wet state models is proposed. Finally, the load variation rate of 5 % and 2 % RCM/min is respectively achieved in the dry/wet state conversion tests on a 350 MW CHP plant based on the proposed control scheme. Thus, the rapid and thorough dry/wet state conversion performance of once-through boiler has successfully improved the operational flexibility of CHP plant under full operating conditions.

An intelligent fuzzy enabled parent node selection approach in low power networks

The Internet of Things lies in establishing low-power and lossy networks created by interconnecting many wireless devices with limited resources. Fascinatingly, an IPv6 routing protocol for low-power and lossy networks has become a common practice for these applications. Even though this protocol addresses the challenges of low-power networks, many issues concerning the quality of service and energy consumption are open to the research community. The protocol relies on a destination-oriented directed acyclic graph, and the root selection depends on some constraints and metrics associated with an objective function (OF). The conventional OFs select parents based on a single metric, such as the expected transmission count or the number of nodes to travel. The current paper proposes an enhancement to the OF metric, aiming to decrease node energy and enhance the quality of service. This improvement is achieved by the factors, including the received signal strength indicator, node distance, power, link quality indicator, and expected transmission count, to select reliable communication links. The minimum power needed for reliable communication is predicted from the received signal strength indicator, node distance, receiver power, and link quality indicator using a nonlinear support vector machine. The OF value of the candidate node is computed from the power level and expected transmission count combined using the Takagi-Sugeno fuzzy model. The proposed OF is implemented in the Cooja simulator and compared against minimum rank with hysteresis OF and OF zero. A considerable improvement in the packet delivery ratio and a 37.5% reduction in energy consumption is obtained.

Classroom Management Effectiveness Assessment Model of English Teaching Based on Gustafson-Kessel Clustering Based Takagi-Sugeno Fuzzy Model with Wild Geese Algorithm

The classroom's evaluation of the teaching effect English has a demanding workload, complex statistics, among other factors. The assessment of classroom instruction quality plays a crucial role in creating a supportive environment that encourages and guides the improvement of university administration and services, as well as in igniting teacher enthusiasm, boosting their efficacy as teachers, and raising the standard of talent development. Strong ambiguity, fuzziness, and inexactness are the main questions that come up while thinking about the case of teaching quality evaluation. The Gustafson - Kessel Clustering Based Takagi-Sugeno Fuzzy (GK-TSF) model along with wild geese algorithm (GKTSF-WGA) method is proposed for classify the skills, attitudes, and verbal, classifications to improve the above issues. Initially, the data’s are gathered via the dataset of teaching assistant evaluation dataset. Afterward, the data’s are fed to pre-processing. In pre-processing segment; it removes the noise and enhances the input data’s utilizing adaptive robust cubature kalman filtering. The feature extraction section receives the output of the preprocessing. Fluency is extracted as a feature using the One - dimensional quantum integer wavelet S-transform in this case. After that, the extracted features are given to the GK-TSF model with wild geese algorithm for effectively classify skills, attitudes, and verbal. The proposed ET-GKTSF-WGA approach is implemented in MATLAB. The proposed ET-GKTSF-WGA method attains 23.05%, 28.95%, and 26.56% higher accuracy for skills; 23.12%, 25.43%, and 27.45%, higher accuracy for attitudes; 25.86%, 27.73%, and 21.04%, higher accuracy for verbal. The computational time performance of the proposed method attains 70.85%, 45.7%, 60.12%, and 35.34%, lower computation. The proposed ET-GKTSF-WGA method is in contrast to the current techniques like ET-FCE, ET-q-ROFs, and ET-K-MC, respectively.