Sugeno Fuzzy Research Articles

Self-Learning Takagi–Sugeno Fuzzy Control With Application to Semicar Active Suspension Model

Self-Learning Takagi–Sugeno Fuzzy Control With Application to Semicar Active Suspension Model

The Selection of KORPRI Scholarship Recipients Using Sugeno's Fuzzy Logic

<p>Student Scholarship Allowance Selection is a routine practice carried out by the Bukittinggi City KORPRI (Civil Servants Corps), which is given to the sons and daughters of civil servants in the Bukittinggi City area who are currently pursuing higher education. Determining scholarship recipients always goes through a strict selection process due to the large number of applicants each year. The selection of scholarship recipients is based on specific criteria and requirements set by KORPRI Bukittinggi City. The criteria/requirements for awarding scholarships are based on three factors: Grade Point Average (GPA), parental income/salary, and number of dependents. In this research, the author applies Sugeno's fuzzy logic in determining scholarship allocation because Sugeno's fuzzy logic can select candidates validly, transparently, and fairly, thus guaranteeing that the selected candidates truly deserve to receive a scholarship. To conduct research effectively, the author designed a research framework that helps the scholarship allocation process using Sugeno fuzzy logic, implemented through the Visual Basic 2010 and MySQL programming languages. Research findings show that the Sugeno fuzzy method can provide accurate and objective recommendations in the scholarship recipient selection process using relevant variables and predetermined rules. This recommendation system can produce fair and transparent decisions. This research aims to contribute to the development of a more effective and efficient scholarship recipient selection system.</p>

Effective Data Aggregation Moel for the Healthcare Data Transmission and Security in Wireless Sensor Network Environment

Data aggregation is the process of collecting and combining information from multiple sources to provide a unified view or summary. In various fields such as statistics, economics, and data analysis, aggregating data helps reveal patterns, trends, or general insights that may not be apparent when examining individual data points. Aggregated data can provide a more comprehensive perspective, facilitating decision-making and strategic planning. This paper explores the application of Sugeno Fuzzy Model Monkey Swarm Optimization (SFMMsO) in healthcare, specifically focusing on data aggregation and security within Wireless Sensor Networks (WSN). The study demonstrates SFMMsO’s efficacy in aggregating patient health data, generating comprehensive Aggregated Health Indices. It also highlights SFMMsO’s optimization capabilities, refining decision variables to enhance healthcare algorithm performance. The paper addresses the critical dimension of security, showcasing SFMMsO’s adaptability in optimizing security parameters and assessing its vulnerability to various attack types. The findings underscore SFMMsO’s potential as a robust tool for healthcare applications, emphasizing the need for proactive security measures to fortify its resilience against potential threats. This study contributes valuable insights into the intersection of optimization algorithms, data aggregation, and security in healthcare, paving the way for advancements in utilizing SFMMsO for secure and comprehensive healthcare systems.

Application Of Fuzzy Logic In Umkm Recommendations Upgrade Using The Sugeno Method At The Cooperative Office Of The City Of Bengkulu

The application of fuzzy logic in the recommendation of MSMEs to upgrade at the Bengkulu City Cooperative Office can help provide assessment results for MSMEs based on assessment criteria so that MSMEs are obtained that are upgraded feasible or not feasible. The Sugeno method will analyze MSME assessment data by looking at the number of workers, capital, and turnover of each MSME which will then calculate the z value in each rule where there are 54 rule compositions used so that the final value for each MSME can be known. Based on the blackbox testing that has been carried out, it is found that the functionality of the MSME recommendation application for upgrading at the Bengkul City Cooperative Service runs well as expected and is able to analyze MSME assessment data through the Sugeno Fuzzy Method to determine recommendations for MSME eligibility for upgrading..

Guaranteed Set-Membership Estimation for Local Nonlinear Uncertain Fuzzy Systems Subject to Partially Decouplable Unknown Inputs

Local nonlinear fuzzy systems are useful for control solutions due to their ability to handle unmeasurable/inexact premise variables and reduce computational complexity. However, their estimation problems still require further development. This paper addresses this issue by investigating set-membership estimation for a class of discrete-time local nonlinear uncertain Takagi–Sugeno fuzzy systems with guaranteed performance. We propose a new observer architecture that solves partially decouplable unknown inputs and converts nonlinear error dynamics into a linear parameter-varying type. Using the systematic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _\infty$</tex-math></inline-formula> -technique, the design conditions in the form of linear matrix inequalities ensure stability and output performance of the state estimation. We also derive a straightforward and effective zonotopic analysis method, considering the fuzzy and local nonlinear context, for less conservative results without using any specific interval set computation. Furthermore, a fast fault detection logic is proposed as an application of the set-membership estimation. Finally, we demonstrate the feasibility and advantages of our approach through three compelling examples, showcasing its efficacy in different scenarios.

Optimizing the Diet of Diabetes Mellitus Sufferers Through Food Mapping Using the Fuzzy Sugeno Method

This research focuses on developing optimal strategies for diet management for Diabetes Mellitus (DM) sufferers by utilizing the Fuzzy Sugeno method in food mapping. Diabetes Mellitus is a chronic metabolic disease that requires a careful management approach to diet to control blood sugar levels. In this study, we designed a system that uses Sugeno's fuzzy principles to categorize foods based on glycemic parameters, nutritional content, and individual patient characteristics. This method allows the formation of fuzzy rules that cover the variability and complexity in the preferences and health needs of each patient. The developed model was tested using DM patient data involving detailed information about diet, medical history and blood sugar response. Experimental results show that this approach can provide more personalized diet recommendations that suit individual health conditions. The application of the Fuzzy Sugeno method in food mapping for DM patients is expected to increase patient compliance with the recommended diet, reduce blood sugar levels, and overall, improve quality of life. Additionally, this approach also provides a foundation for the development of adaptive dietary management systems, which can continuously adapt to changing patient health needs over time.

A Novel Learning Approach to Remove Oscillations in First‐Order Takagi–Sugeno Fuzzy System: Gradient Descent‐Based Neuro‐Fuzzy Algorithm Using Smoothing Group Lasso Regularization

AbstractAs a universal approximator, the first order Takagi–Sugeno fuzzy system possesses the capability to approximate widespread nonlinear systems through a group of IF THEN fuzzy rules. Although group lasso regularization has the advantage of inducing group sparsity and handling variable selection issues, it can lead to numerical oscillations and theoretical challenges in calculating the gradient at the origin when employed directly during training. The paper addresses the aforementioned obstacle by invoking a smoothing function to approximate group lasso regularization. On this basis, a gradient‐based neuro fuzzy learning algorithm with smoothing group lasso regularization for the first order Takagi–Sugeno fuzzy system is proposed. The convergence of the proposed algorithm is rigorously proved under gentle conditions. In addition, experimental outcomes acquired on two approximations and two classification simulations demonstrate that the proposed algorithm outperforms the algorithm with original group lasso regularization and L2 regularization in terms of error, pruned neurons, and accuracy. This is particularly evident in significant advancements in pruned neurons due to group sparsity. In comparison to the algorithm with L2 regularization, the proposed algorithm exhibits improvements of 6.3, 5.3, and 142.6 in pruned neurons during sin function, Gabor function, and Sonar benchmark dataset simulations, respectively.

Distributed event-triggered fuzzy control for nonlinear interconnected systems

This paper deals with the asynchronous distributed event-triggered control of continuous-time nonlinear interconnected systems. The nonlinear dynamics of the subsystems are represented by Takagi–Sugeno fuzzy models via the sector nonlinearity approach. Moreover, the nonlinear interconnections among the subsystems are assumed to be known and sector-bounded. In the event-triggered control setup, where the state information is asynchronously available to the local controllers only in specific time instants, it is necessary to deal with the asynchronous premise variables because they introduce extra difficulties to derive suitable co-design conditions. To deal with this issue, we propose a new triggering strategy such that the local event-triggering mechanisms (ETMs) counteract the effects of asynchronous premise variables. With this new distributed ETM, a co-design condition is proposed, and the existence of a strictly positive minimum inter-event time is proved to exclude Zeno behaviour. Moreover, a multi-objective optimization procedure is introduced to enlarge the estimate of the domain of attraction of the closed-loop equilibrium and minimize the number of transmissions provided by the ETMs. Finally, the approach is validated through the synchronization of interconnected oscillators.

Formula omitted]-[formula omitted]-dissipative control of T-S fuzzy systems via aperiodic sampling control with communication delay

This article concerns the strict (Q,S,R)-γ dissipativity of Takagi–Sugeno fuzzy systems (TSFSs) via aperiodic sampled-data control with communication delay. The motivations of this paper are that the transmission channels have communication delay and the original dissipative analysis method may be conservative. In order to consider the impact of communication delays, first of all, a sampled-data controller (SDC) with a constant communication delay is conceived to analyze the dissipativity of TSFSs. Next, the overall closed-loop system model is established stemming from the designed controller. In order to attain less conservative results, a new looped-functional which can take advantage of the available information of the sampling instants is formed. Then, strict (Q,S,R)-γ dissipativity can be guaranteed by a criterion obtained by using the looped-functional and the modified free-matrix-based inequality (FMBI) technique. By working out the obtained linear matrix inequalities (LMIs), the controller gain matrix that meets the requirement can be determined. Finally, the truck trailer and the mass–spring systems are showcased as simulation examples to provide evidence of the effectivity and superiority of the devised approach. In the presence of 0.1 s communication delay in the truck trailer system controller, the maximum γ can reach 0.9751 when sampling interval h takes 0.15 s, which makes the system can achieve strict (Q,S,R)-γ dissipativity.

A Takagi–Sugeno fuzzy controller for minimizing cancer cells with application to androgen deprivation therapy

Androgen deprivation therapy (ADT) is frequently used to treat prostate cancer which is a widespread disease having a very low survival rate. A prolonged course of ADT can increase toxicity and drug resistance. This study proposes an adaptive therapy combining chemotherapy or immunotherapy with the discontinuation of hormone therapy to overcome these obstacles. The super-twisting sliding mode control (STSMC) algorithm is found to be one of the effective approach as an ADT model for obtaining suitable dosage adaptively. The primary objective is to rapidly reduce the number of cancer cells and the duration of drug exposure. The Takagi–Sugeno fuzzy controller-based active control algorithm is introduced, and it’s performance is compared with the STSMC algorithm. While maintaining global asymptotic stability, the Takagi–Sugeno fuzzy controller reduces the duration of therapy to six months. The controllers are implemented utilizing the linear matrix inequality (LMI) algorithm and the yet another LMI (YALMIP) toolset for MATLAB, and their efficacy is validated utilizing MATLAB and Simulink simulations. This study presents a novel approach to improve prostate cancer treatment outcomes by integrating nonlinear control algorithms and adaptive dosage strategies to reduce treatment duration and minimize drug exposure, thereby improving patient outcomes in prostate cancer management.

Proportional multi-integral observer design for Takagi–Sugeno systems with unmeasurable premise variables: Conservatism reduction via polyquadratic Lyapunov function

In this paper, we present the design of a new unknown-input observer for Takagi–Sugeno fuzzy systems with unmeasurable premise variables. The work proposes an improvement of the simultaneous state and unknown-input observer. The main idea is to consider a non-quadratic Lyapunov function in order to reduce the conservatism of its quadratic counterpart. Moreover, the mean value theorem is used to express the error dynamics in a way that reduces the conservatism of the bounded terms assumptions. The stability conditions are formulated as Bilinear Matrix Inequalities. To address these, we propose an iterative algorithm based on linear matrix inequalities, which transforms the Bilinear form into a sets of linear matrix inequalities. Finally, two examples are given to highlight the performance of the proposed observer.

An Improved Zonotopic Approach Applied to Fault Detection for Takagi–Sugeno Fuzzy Systems

In this work, an actuator fault detection problem for discrete-time Takagi-Sugeno fuzzy systems is tackled in a bounded error context where both state disturbances and measurement noise are assumed to be unknown but bounded with known bounds. First, a peak-to-peak performance synthesis method is applied to design a robust residual generator against the considered process disturbances and measurement noise. Meanwhile, an improved zonotopic approach is proposed to compute tight adaptive thresholds for residual evaluation. Then, a reliable set-membership fault detection strategy with the aid of generated residual signals and adaptive thresholds is introduced. Finally, the viability of the proposed method is demonstrated via a numerical simulation. Then, an experimentation on a 3D Crane system is performed to show its practicability.

Determining The Selling Price of Thrift Using The Fuzzy Sugeno Method

The use of thrift goods in life has the effect of saving and loving the environment. Many of these objects are difficult to degrade in nature and are nevertheless thought to have economic worth, for example used electronic equipment such as laptops. On the other hand, in today's classroom environment, laptops are quite important. Even the most recent computers with good features are fairly costly, thus used laptops are one answer to this. The seller's selling price usually only takes a few elements into account, therefore the price set does not always match the requirements. The aim of this paper is to apply the zero order Sugeno fuzzy approach to determine the selling price of old laptop. The system is built with characteristics such as laptop age, physical condition, RAM, new purchase price, and used selling price. The simulation findings suggest that fuzzy logic employing the zero-order Sugeno approach can be utilized to determine the selling price of old laptop while accounting for the affecting variables.

Mamdani vs. Takagi-Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models.

The transition to intelligent transportation systems (ITSs) is necessary to improve traffic flow in urban areas and reduce traffic congestion. Traffic modeling simplifies the understanding of the traffic paradigm and helps researchers to estimate traffic behavior and identify appropriate solutions for traffic control. One of the most used traffic models is the car-following model, which aims to control the movement of a vehicle based on the behavior of the vehicle ahead while ensuring collision avoidance. Differences between the simulated and observed model are present because the modeling process is affected by uncertainties. Furthermore, the measurement of traffic parameters also introduces uncertainties through measurement errors. To ensure that a simulation model fully replicates the observed model, it is necessary to have a calibration process that applies the appropriate compensation values to the simulation model parameters to reduce the differences compared to the observed model parameters. Fuzzy inference techniques proved their ability to solve uncertainties in continuous-time models. This article aims to provide a comparative analysis of the application of Mamdani and Takagi-Sugeno fuzzy inference systems (FISs) in the calibration of a continuous-time car-following model by proposing a methodology that allows for parallel data processing and the determination of the simulated model output resulting from the application of both fuzzy techniques. Evaluation of their impact on the follower vehicle considers the running distance and the dynamic safety distance based on the observed behavior of the leader vehicle. In this way, the identification of the appropriate compensation values to be applied to the input of the simulated model has a great impact on the development of autonomous driving solutions, where the real-time processing of sensor data has a crucial impact on establishing the car-following strategy while ensuring collision avoidance. This research performs a simulation experiment in Simulink (MATLAB R2023a, Natick, MA, USA: The MathWorks Inc.) and considers traffic data collected by inductive loops as parameters of the observed model. To emphasize the role of Mamdani and Takagi-Sugeno FISs, a noise injection is applied to the model parameters with the help of a band-limited white-noise Simulink block to simulate sensor measurement errors and errors introduced by the simulation process. A discussion based on performance evaluation follows the simulation experiment, and even though both techniques can be successfully applied in the calibration of the car-following models, the Takagi-Sugeno FIS provides more accurate compensation values, which leads to a closer behavior to the observed model.

Dynamic event–triggered security control for stochastic partial differential equation systems via a unified pointwise measurement/control strategy

This article focuses on the security control for parabolic partial differential equation systems driven by fractional Brownian motion based on a unified pointwise measurement/control strategy. Initially, by employing the Takagi–Sugeno fuzzy rule, nonlinear stochastic systems are transformed into a series of linear subsystems. Then, based on the improved dynamic event-triggered mechanism and pointwise measurements method, a security controller is presented to resist deception attacks. In addition, the Lyapunov direct method and some novel inequalities are used to ensure the considered system is mean-square exponentially stable, and the gain of the point controller is obtained. Finally, an application example is provided to verify the effectiveness of the proposed method.

Channel-Level Event-Triggered Communication Scheme for Path Tracking Control of Autonomous Ground Vehicles With Distributed Sensors

In this paper, a channel-level event-triggered communication scheme is investigated for path tracking control of autonomous ground vehicles via an in-vehicle network to ensure path tracking performance while reducing network resource utilization. First, a path tracking system is established considering the effect of norm bounded uncertainty on lateral vehicle dynamics, and a state feedback path tracking controller is designed within the Takagi–Sugeno fuzzy framework. Next, a decentralized event-triggered communication scheme is adopted between the sensors and controller, which makes it possible to save communication resources at the channel level. Finally, the results in the CarSim-Simulink joint-simulation environment show that the proposed controller can ensure a reliable path tracking performance in both lateral offset and heading error. At the same time, compared with the simple time-triggered scheme, the proposed method can effectively reduce the usage of communication resources by more than 80%.

Finite-time H∞ control for a class of Takagi–Sugeno fuzzy singular systems

This article studies the finite-time stabilization and finite-time H∞ control problems for a class of Takagi–Sugeno fuzzy singular systems and proposes a finite-time H∞ fuzzy controller to enable the system to cope with external disturbances, and the closed-loop system state converges to the equilibrium point. Before designing the controller, impulse controllability of the Takagi–Sugeno fuzzy singular system is discussed. Then, a finite-time H∞ fuzzy controller is designed, and the finite-time stabilization conditions of the Takagi–Sugeno fuzzy singular system are expressed by linear matrix inequality. Finally, the simulation outcomes of a nonlinear singular system demonstrate that the designed controller has a good suppression effect on external disturbances, and the closed-loop system state converges to the equilibrium point in finite-time T and remains at the equilibrium point after T.

Resilient Event-Triggered Dynamic Output Feedback Control for Networked Takagi–Sugeno Fuzzy Systems Under Denial-of-Service Attacks

Resilient Event-Triggered Dynamic Output Feedback Control for Networked Takagi–Sugeno Fuzzy Systems Under Denial-of-Service Attacks

Improved Admissibility Criteria for Takagi–Sugeno Fuzzy Singular Systems With Time-Varying Delay

The article studies the admissibility stability issue of Takagi-Sugeno fuzzy singular(TFS) systems with time-varying delay. First, negative-definiteness constraints for nth-order matrix-valued polynomials are established based on Finsler's Lemma. A new reciprocally convex inequality(RCI) is developed according to the proposed negative-definiteness method. Then, an improved state decomposition Lyapunov-Krasovskii(L-K) functional is constructed based on the decomposed state method. The improved L-K functional is augmented by considering more information on TFS system states and the delay-dependent matrix. Based on the novel RCI and the L-K functional, a less conservative admissibility criterion is derived. Finally, a well-studied numerical example is performed to reveal the superiority and validity of the proposed admissibility criteria.

Reliable Sampling Mechanism for Takagi–Sugeno Fuzzy NCSs Under Deception Cyberattacks for the Application of the Inverted Pendulum System

This article investigates the stability problem of Takagi–Sugeno fuzzy networked control systems (TSNCSs) under deception cyberattacks via a reliable sampling mechanism, which has important research value for applications in network security. First, a fuzzy weight functional method is introduced, and a new Lyapunov–Krasovskii functional is developed, which better incorporates nonlinear problems in the model. Then, in order to reduce the initial constraints, improved time delay closed-loop functions are constructed that consider the delay information and the characteristics of the sampling time points. Furthermore, considering the reliability issues of controllers in real industry, we establish some sufficient conditions and implement a novel reliable sampling controller with deception attacks (DAs) to control the asymptotic stability of TSNCS. Finally, the correctness and feasibility of the proposed method are verified experimentally using an inverted pendulum system.