Zeitschriftenartikel: „Takagi Sugeno (TS) fuzzy models“

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Alshammari, Badr, Rim Ben Salah, Omar Kahouli und Lioua Kolsi. „Design of Fuzzy TS-PDC Controller for Electrical Power System via Rules Reduction Approach“. Symmetry 12, Nr. 12 (12.12.2020): 2068. http://dx.doi.org/10.3390/sym12122068.

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In this paper, a new Takagi–Sugeno Fuzzy Logic controller (TS-FLC) is presented and applied for modeling and controlling the nonlinear power systems even in the presence of disturbances. Firstly, a nonlinear mathematical model for the electrical power system is presented with consideration of PSS and AVR controller. Then, a Takagi–Sugeno Fuzzy Logic controller is employed to control power system stability. Nevertheless, the study of the stability of Takagi–Sugeno fuzzy models will be difficult in the case where the number of nonlinearities is important. To cope with this problem, this study proposed a methodology to reduce the number of rules and to guarantee the global stability of the power system. The new model included only two rules. All the other nonlinearities were considered as uncertainties. In addition, a Parallel Distributed Compensation controller is designed using the Linear Matrix Inequalities constraints in order to guarantee system stability. Finally, this approach is applied on a Single Machine Infinite Bus affected by fault perturbation. To show the novelty of Takagi Sugeno’s method, we compared our approach to the Taylor linearization method. The numerical simulations prove the feasibility and performance of the proposed method.

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Chiu und Peng. „Design of Takagi-Sugeno Fuzzy Control Scheme for Real World System Control“. Sustainability 11, Nr. 14 (15.07.2019): 3855. http://dx.doi.org/10.3390/su11143855.

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In this study, a novelty dual Takagi-Sugeno (TS) fuzzy control scheme (DTSFCS) is proposed for real world system control. We propose using a ball robot (BR) system control problem, where the BR has the ability to move omnidirectionally. The proposed control scheme combines two fuzzy control approaches for a BR. In this fuzzy control approach, the TS fuzzy model was adopted for the fuzzy modeling of the BR. The concept of parallel distributed compensation (PDC) was utilized to develop a fuzzy control scheme from the TS fuzzy models. The linear matrix inequalities (LMIs) can formulate sufficient conditions. Moreover, in this study, the motors of the BR were mounted on two orthogonal axes. Then, the dual TS fuzzy controller was designed to independently operate without coupling. Finally, the efficiency of the proposed control scheme is illustrated by the experimental and simulation results that are presented in this study.

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Houili, Rabiaa, Mohamed Yacine Hammoudi, Mohamed Benbouzid und Abdennacer Titaouine. „Observer-Based Controller Using Line Integral Lyapunov Fuzzy Function for TS Fuzzy Systems: Application to Induction Motors“. Machines 11, Nr. 3 (10.03.2023): 374. http://dx.doi.org/10.3390/machines11030374.

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This paper deals with the stabilization problem of a nonlinear system described by a Takagi–Sugeno fuzzy (TSF) model with unmeasurable premise variables via a robust controller. Applying the sector nonlinearity techniques, the nonlinear system is represented by a decoupled fuzzy model. Then, we design a robust observer-based controller for the obtained fuzzy system by utilizing the differential mean value approach. The observer and controller gains are obtained by the separation principle, in which the problem is solved in the sum of linear matrix inequalities (LMIs). The paper presents two main contributions: A state feedback controller is designed using differential mean value (DMVT) which ensures robust stabilization of the nonlinear system. Additionally, the Luenberger observer is extended to the Takagi–Sugeno fuzzy models. The second contribution is to reduce conservatism in the obtained conditions, a non-quadratic Lyapunov function (known as the line integral Lyapunov fuzzy candidate (LILF)) is employed. Two examples are provided to further illustrate the efficiency and robustness of the proposed approach; specifically, the Takagi–Sugeno fuzzy descriptor of an induction motor is derived and a robust observer-based controller applied to the original nonlinear system.

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Youssef, T., M. Chadli, H. R. Karimi und M. Zelmat. „Chaos Synchronization Based on Unknown Input Proportional Multiple-Integral Fuzzy Observer“. Abstract and Applied Analysis 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/670878.

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This paper presents an unknown input Proportional Multiple-Integral Observer (PIO) for synchronization of chaotic systems based on Takagi-Sugeno (TS) fuzzy chaotic models subject to unmeasurable decision variables and unknown input. In a secure communication configuration, this unknown input is regarded as a message encoded in the chaotic system and recovered by the proposed PIO. Both states and outputs of the fuzzy chaotic models are subject to polynomial unknown input withkth derivative zero. Using Lyapunov stability theory, sufficient design conditions for synchronization are proposed. The PIO gains matrices are obtained by resolving linear matrix inequalities (LMIs) constraints. Simulation results show through two TS fuzzy chaotic models the validity of the proposed method.

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Ellouze, Ameni, Omar Kahouli, Mohamed Ksantini, Ali Rebhi, Nidhal Hnaien und François Delmotte. „Continuous Stability TS Fuzzy Systems Novel Frame Controlled by a Discrete Approach and Based on SOS Methodology“. Mathematics 9, Nr. 23 (04.12.2021): 3129. http://dx.doi.org/10.3390/math9233129.

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Generally, the continuous and discrete TS fuzzy systems’ control is studied independently. Unlike the discrete systems, stability results for the continuous systems suffer from conservatism because it is still quite difficult to apply non-quadratic Lyapunov functions, something which is much easier for the discrete systems. In this paper and in order to obtain new results for the continuous case, we proposed to connect the continuous with the discrete cases and then check the stability of the continuous TS fuzzy systems by means of the discrete design approach. To this end, a novel frame was proposed using the sum of square approach (SOS) to check the stability of the continuous Takagi Sugeno (TS) fuzzy models based on the discrete controller. Indeed, the control of the continuous TS fuzzy models is ensured by the discrete gains obtained from the Euler discrete form and based on the non-quadratic Lyapunov function. The simulation examples applied for various models, by modifying the order of the Euler discrete fuzzy system, are presented to show the effectiveness of the proposed methodology.

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Kamal, Elkhatib, Magdy Koutb, Abdul Azim Sobaih und Sahar Kaddah. „Maximum Power Control of Hybrid Wind-Diesel-Storage System“. Advances in Fuzzy Systems 2008 (2008): 1–9. http://dx.doi.org/10.1155/2008/963710.

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Extraction of maximum wind power of variable speed wind turbines in hybrid wind-diesel-storage system (HWDSS) is considered due to economical purposes. The proposed control algorithm utilizes extended fuzzy-linear matrix equalities (FLMEs) systems design of stabilizing fuzzy controllers for nonlinear systems described by Takagi-Sugeno (TS) fuzzy models. The algorithm maximizes the power coefficient for a fixed pitch. Moreover, it reduces the voltage ripple and stabilizes the system over a wide range of wind speed variations. The control scheme is tested for different profiles of wind speed pattern and provides satisfactory results.

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Akgun, O. Burak, und Elcin Kentel. „Ensemble Precipitation Estimation Using a Fuzzy Rule-Based Model“. Engineering Proceedings 5, Nr. 1 (09.07.2021): 48. http://dx.doi.org/10.3390/engproc2021005048.

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In this study, a Takagi-Sugeno (TS) fuzzy rule-based (FRB) model is used for ensembling precipitation time series. The TS FRB model takes precipitation predictions of grid-based regional climate models (RCMs) from the EUR11 domain, available from the CORDEX database, as inputs to generate ensembled precipitation time series for two meteorological stations (MSs) in the Mediterranean region of Turkey. For each MS, RCM data that are available at the closest grid to the corresponding MSs are used. To generate the fuzzy rules of the TS FRB model, the subtractive clustering algorithm (SC) is utilized. Together with the TS FRB, the simple ensemble mean approach is also applied, and the performances of these two model results and individual RCM predictions are compared. The results show that ensembled models outperform individual RCMs, for monthly precipitation, for both MSs. On the other hand, although ensemble models capture the general trend in the observations, they underestimate the peak precipitation events.

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Filasová, Anna, und Dušan Krokavec. „H∞Control of Pairwise Distributable Large-Scale TS Fuzzy Systems“. Mathematical Problems in Engineering 2013 (2013): 1–18. http://dx.doi.org/10.1155/2013/874085.

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The paper presents new conditions suitable in design of the stabilizing state controller for a class of continuous-time nonlinear systems, which are representable by pairwise distributable Takagi-Sugeno models. Taking into account the affine properties of the TS model structure and applying the pairwise subsystems fuzzy control scheme relating to the parallel distributed output compensators, the extended bounded real lemma form and the sufficient design conditions for pairwise decentralized control are outlined in terms of linear matrix inequalities. The proposed procedure decouples the Lyapunov matrix and the system parameter matrices in the LMIs and, using free tuning parameter, provides the way to obtain global stability of such large-scale TS systems and optimizes subsystems interactionH∞norm bounds.

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Zahra, Taif, Lafifi M. Mourad und Abbassi Haj Ahmed. „Robust Fuzzy Sliding Mode Observer for Faults Detection in Solar Power Plant Application“. Instrumentation Mesure Métrologie 19, Nr. 4 (30.09.2020): 281–87. http://dx.doi.org/10.18280/i2m.190405.

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This article deals with the designing of fuzzy sliding mode observer in order to fault diagnosis in solar power plant. Purpose technique enable to the modulated using Takagi-Sugeno (TS) fuzzy models. Principal of proposed observer is to uses for estimate the state vector of the system; a Linear Matrix Inequalities (LMIs) is performed to ensure stability conditions. Foremother, it is deriving a diagnosis signal-residual. The residual is generated by the comparison of measured and estimated output. Proposed approach performance is tested in solar power plant model through numerical results.

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Dec, Grzegorz, Grzegorz Drałus, Damian Mazur und Bogdan Kwiatkowski. „Forecasting Models of Daily Energy Generation by PV Panels Using Fuzzy Logic“. Energies 14, Nr. 6 (17.03.2021): 1676. http://dx.doi.org/10.3390/en14061676.

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This paper contains studies of daily energy production forecasting methods for photovoltaic solar panels (PV panel) by using mathematical methods and fuzzy logic models. Mathematical models are based on analytic equations that bind PV panel power with temperature and solar radiation. In models based on fuzzy logic, we use Adaptive-network-based Fuzzy Inference Systems (ANFIS) and the zero-order Takagi-Sugeno model (TS) with specially selected linear and non-linear membership functions. The use of mentioned membership functions causes that the TS system is equivalent to a polynomial and its properties can be compared to other analytical models of PV panels found in the literature. The developed models are based on data from a real system. The accuracy of developed prognostic models is compared, and a prototype software implementing the best-performing models is presented. The software is written for a generic programmable logic controller (PLC) compliant to the IEC 61131-3 standard.

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Ellouze, Ameni, François Delmotte, Jimmy Lauber, Mohamed Chtourou und Mohamed Ksantini. „Decay rate performance approach for stabilization continuous fuzzy models using their discretized forms“. International Journal of Intelligent Computing and Cybernetics 8, Nr. 4 (09.11.2015): 383–400. http://dx.doi.org/10.1108/ijicc-06-2015-0020.

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Purpose – The purpose of this paper is to deal with the stabilization of the continuous Takagi Sugeno (TS) fuzzy models using their discretized forms based on the decay rate performance approach. Design/methodology/approach – This approach is structured as follows: first, a discrete model is obtained from the discretization of the continuous TS fuzzy model. The discretized model is obtained from the Euler approximation method which is used for several orders. Second, based on the decay rate stabilization conditions, the gains of a non-PDC control law ensuring the stabilization of the discrete model are determined. Third by keeping the values of the gains, the authors determine the values of the performance criterion and the authors check by simulation the stability of the continuous TS fuzzy models through the zero order hold. Findings – The proposed idea lead to compare the performance continuous stability results with the literature. The comparison is, also, taken between the quadratic and non-quadratic cases. Originality/value – Therefore, the originality of this paper consists in the improvement of the continuous fuzzy models by using their discretized models. In this case, the effect of the discretization step on the performances of the continuous TS fuzzy models is studied. The usefulness of this approach is shown through two examples.

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et al., Alkaik. „A Takagi-Sugeno model approach for robust fuzzy control design for trajectory tracking of non-linear systems“. International Journal of ADVANCED AND APPLIED SCIENCES 9, Nr. 3 (März 2022): 159–64. http://dx.doi.org/10.21833/ijaas.2022.03.018.

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This article investigates the robust fuzzy tracking control design for a class of uncertain nonlinear systems using the Takagi–Sugeno (TS) fuzzy models. The main purpose of this study is to design state feedback and observer-based controllers such that the closed-loop system is asymptotically stable. Based on the Lyapunov theory, sufficient conditions are derived such that the closed-loop system is robustly stable. The linear matrix inequality LMI approach is used to obtain the state-feedback and observer gains. The effectiveness of the proposed design approach is provided via numerical simulations for a pendulum system.

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et al., Alsaket. „Robust fuzzy control for non-linear systems with uncertainties: A Takagi- Sugeno model approach“. International Journal of ADVANCED AND APPLIED SCIENCES 9, Nr. 5 (Mai 2022): 32–36. http://dx.doi.org/10.21833/ijaas.2022.05.004.

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This article studies the problem of robust control design for a class of uncertain nonlinear systems using the Takagi–Sugeno (TS) fuzzy models. The objective of this study is to design state feedback and an observer-based controller such that the closed-loop system is asymptotically stable. For this purpose, sufficient conditions are derived, and the corresponding controllers are designed by solving a set of linear matrix inequalities (LMIs). The effectiveness of the proposed design approach is provided via numerical simulations for a permanent magnet synchronous motor (PMSM).

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Himmelsbach, Matthias, und Andreas Kroll. „On Optimal Test Signal Design and Parameter Identification Schemes for Dynamic Takagi-Sugeno Fuzzy Models Using the Fisher Information Matrix“. International Journal of Fuzzy Systems 24, Nr. 2 (21.11.2021): 1012–24. http://dx.doi.org/10.1007/s40815-021-01185-9.

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AbstractThis paper is concerned with the analysis of optimization procedures for optimal experiment design for locally affine Takagi-Sugeno (TS) fuzzy models based on the Fisher Information Matrix (FIM). The FIM is used to estimate the covariance matrix of a parameter estimate. It depends on the model parameters as well as the regression variables. Due to the dependency on the model parameters good initial models are required. Since the FIM is a matrix, a scalar measure of the FIM is optimized. Different measures and optimization goals are investigated in three case studies.

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Tapia-Herrera, Ricardo, Jesús Alberto Meda-Campaña, Samuel Alcántara-Montes, Tonatiuh Hernández-Cortés und Lizbeth Salgado-Conrado. „Tuning of a TS Fuzzy Output Regulator Using the Steepest Descent Approach and ANFIS“. Mathematical Problems in Engineering 2013 (2013): 1–14. http://dx.doi.org/10.1155/2013/873430.

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The exact output regulation problem for Takagi-Sugeno (TS) fuzzy models, designed from linear local subsystems, may have a solution if input matrices are the same for every local linear subsystem. Unfortunately, such a condition is difficult to accomplish in general. Therefore, in this work, an adaptive network-based fuzzy inference system (ANFIS) is integrated into the fuzzy controller in order to obtain the optimal fuzzy membership functions yielding adequate combination of the local regulators such that the output regulation error in steady-state is reduced, avoiding in this way the aforementioned condition. In comparison with the steepest descent method employed for tuning fuzzy controllers, ANFIS approximates the mappings between local regulators with membership functions which are not necessary known functions as Gaussian bell (gbell), sigmoidal, and triangular membership functions. Due to the structure of the fuzzy controller, Levenberg-Marquardt method is employed during the training of ANFIS.

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Krokavec, Dušan, und Anna Filasová. „Stabilizing Fuzzy Output Control for a Class of Nonlinear Systems“. Advances in Fuzzy Systems 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/294971.

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The paper presents new conditions suitable in design of a stabilizing output controller for a class of continuous-time nonlinear systems, represented by Takagi-Sugeno models. Taking into account the affine properties of the TS model structure and applying the fuzzy control scheme relating to the parallel distributed output compensators, the sufficient design conditions are outlined in terms of linear matrix inequalities. The proposed procedure decouples the Lyapunov matrix and the system parameter matrices in the LMIs and guarantees global stability of the system. Simulation result illustrates the design procedure and demonstrates the performances of the proposed design method.

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Blanco, Yann, Wilfrid Perruquetti und Pierre Borne. „Stability and stabilization of nonlinear systems and Takagi-Sugeno's fuzzy models“. Mathematical Problems in Engineering 7, Nr. 3 (2001): 221–40. http://dx.doi.org/10.1155/s1024123x01001624.

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This paper outlines a methodology to study the stability of Takagi-Sugeno's (TS) fuzzy models. The stability analysis of the TS model is performed using a quadratic Liapunov candidate function. This paper proposes a relaxation of Tanaka's stability condition: unlike related works, the equations to be solved are not Liapunov equations for each rule matrix, but a convex combination of them. The coefficients of this sums depend on the membership functions. This method is applied to the design of continuous controllers for the TS model. Three different control structures are investigated, among which the Parallel Distributed Compensation (PDC). An application to the inverted pendulum is proposed here.

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Liu, Ling, Bao Guo Tang und Kai Sun. „The Output Power of the PV Power Plant Modeling Based on ANFIS“. Advanced Materials Research 1006-1007 (August 2014): 945–54. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.945.

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To find an effective and reasonable method for calculating precisely the output power of the PV power plant, adaptive neuro-fuzzy inference system (ANFIS) based on Takagi-Sugeno (TS) is proposed. Analysis of the various weather factors that affect the output power of the PV power plant, and select the appropriate input ,MATLAB as a tool ,depend on the different input variable to establish different output power of photovoltaic power plants based on the subtractive clustering the ANFIS model .Results show that all the model has a high accuracy and meet the practical engineering application requirements,by comparing models choose the optimal model.

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Assawinchaichote, Wudhichai. „Further results on robust fuzzy dynamic systems with LMI D-stability constraints“. International Journal of Applied Mathematics and Computer Science 24, Nr. 4 (01.12.2014): 785–94. http://dx.doi.org/10.2478/amcs-2014-0058.

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Abstract This paper examines the problem of designing a robust H∞ fuzzy controller with D-stability constraints for a class of nonlinear dynamic systems which is described by a Takagi-Sugeno (TS) fuzzy model. Fuzzy modelling is a multi-model approach in which simple sub-models are combined to determine the global behavior of the system. Based on a linear matrix inequality (LMI) approach, we develop a robust H∞ fuzzy controller that guarantees (i) the L2-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value, and (ii) the closed-loop poles of each local system to be within a specified stability region. Sufficient conditions for the controller are given in terms of LMIs. Finally, to show the effectiveness of the designed approach, an example is provided to illustrate the use of the proposed methodology.

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Shams, Zahra, und Aref Shahmansoorian. „Fault estimation based on observer for chaotic Lorenz system with bifurcation problem“. Transactions of the Institute of Measurement and Control 42, Nr. 3 (20.10.2019): 576–85. http://dx.doi.org/10.1177/0142331219879267.

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In this paper, the simultaneous estimation of the process and sensor fault of a chaotic Lorenz system in a noisy environment is investigated. The problem of the process fault leads to the occurrence of a bifurcation in the Lorenz system. The purpose of this article is to combine the concept of fault and bifurcation. Fault diagnosis of nonlinear systems becomes more practicable when it is managed over Takagi-Sugeno (TS) approximated fuzzy models. TS fuzzy model unknown input observer can estimate faults and states. In this respect, a TS fuzzy model augmented by a proportional plus integral, for fault modeling observer (FO) is lined up for the estimation of the unmeasured signal. The simulation conclusions hint that the observer runs well in estimating process fault, states, and sensor fault. Using these estimates, the deviation value of a parameter is determined from its actual value, which is the same as the low amount of deviation in this article because the bifurcation occurs in the system. In the first part of the simulation, the process fault occurs and drives system behavior into chaos, and the bifurcation diagram uses to explain it. In the second part, the system is influenced by actuator fault. The conclusion is validated through extensive simulations.

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JAMWAL, PRASHANT, S. Q. XIE, SHAHID HUSSAIN und KEAN AW. „MODELING PNEUMATIC MUSCLE ACTUATORS: ARTIFICIAL INTELLIGENCE APPROACH“. International Journal of Information Acquisition 07, Nr. 02 (Juni 2010): 151–64. http://dx.doi.org/10.1142/s0219878910002130.

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Robot human interaction requires use of safe, compliant and light weight actuators. Conventional linear motors and pneumatic cylinders are normally used to actuate robots to assist and augment human motions. Lately it has been realized that these actuators are not suitable and safe for applications involving human actor. Their large weight, size and stiffer design raise concerns. Pneumatic muscle actuators (PMA) on the other hand are very light weight, compact and have inherent compliance which make them potential candidate for applications involving robot human interaction. Taking on the advantages, these actuators are now being experimented for a variety of medical and rehabilitation applications. However they are not very popular due to their highly nonlinear and time dependent behavior which poses control problems. In this paper, an attempt is being made to accurately predict the uncertain and ambiguous characteristics of PMA using Artificial Intelligence (AI). Conventional tools such as analytical and numerical methods can only model a nonlinear system which is time independent. Time varying nonlinear system characteristics can be best modeled using artificial intelligence-based regression models. In this research, Artificial Neural Network (ANN), Mamdani Fuzzy Inference System (FIS) and Takagi-Sugeno (TS)-based fuzzy system are developed after carefully analyzing the time series data obtained from a real system. To achieve higher accuracy from these models, their parameters are tuned. Parameters of ANN are tuned using back propagation algorithm whereas fuzzy parameters are tuned using three different methods, namely, gradient descent method (GD), genetic algorithms (GA) and Modified Genetic Algorithm (MGA). It was found that the TS fuzzy inference system tuned by MGA provides better accuracy and can also model the time dependent behavior of PMA. The proposed TS fuzzy system is found to perform better in terms of accuracy and maximum deviation when compared to the previous approaches in the literature.

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Farah, Nabil, M. H. N. Talib, Z. Ibrahim, J. M. Lazi und Maaspaliza Azri. „Self-tuning Fuzzy Logic Controller Based on Takagi-Sugeno Applied to Induction Motor Drives“. International Journal of Power Electronics and Drive Systems (IJPEDS) 9, Nr. 4 (01.12.2018): 1967. http://dx.doi.org/10.11591/ijpeds.v9.i4.pp1967-1975.

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<span>Fuzzy logic controller has been the main focus for many researchers and industries in motor drives. The popularity of Fuzzy Logic Controller (FLC) is due to its reliability and ability to handle parameters changes during load or disturbance. Fuzzy logic design can be visualized in two categories, mamdani design or Takagi-Sugeno (TS). Mamdani type can facilitate the design process, however it require high computational burden especially with big number of rules and experimental testing. This paper, develop Self-Tuning (ST) mechanism based on Takagi-Sugeno (TS) fuzzy type. The mechanism tunes the input scaling factor of speed fuzzy control of Induction Motor (IM) drives Based on the speed error and changes of error. A comparison study is done between the standard TS and the ST-TS based on simulations approaches considering different speed operations. Speed response characteristics such as rise time, overshoot, and settling time are compared for ST-TS and TS. It was shown that ST-TS has optimum results compared to the standard TS. The significance of the proposed method is that, optimum computational burden reduction is achieved.</span>

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Iqbal Ahammed A.K. und Mohammed Fazle Azeem. „Robust Stabilization And Control Of Takagi-Sugeno Fuzzy Systems With Parameter Uncertainties And Disturbances Via State Feedback And Output Feedback“. International Journal of Fuzzy System Applications 9, Nr. 3 (Juli 2020): 63–99. http://dx.doi.org/10.4018/ijfsa.2020070104.

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Most of the systems in the industry contain extreme non-linearity and uncertainties, which are hard to design and control utilizing general nonlinear systems. To conquer this sort of troubles, different plans have been produced in the most recent two decades, among which a popular methodology is Takagi-Sugeno fuzzy control. In this article, we present robust stabilization and control of Takagi-Sugeno (T-S) fuzzy systems with parameter uncertainties and disturbances. Initially, Takagi and Sugeno (TS) fuzzy model is used to represent a nonlinear system. Based on this T-S fuzzy model, fuzzy controller design schemes for state feedback and output feedback is also developed. Then, necessary conditions are derived for robust stabilization in the intelligence of Lyapunov asymptotic stability and are expressed in the arrangement of linear matrix inequalities (LMIs). The proposed system is implemented in the working platform of MATLAB and the simulation results are provided to illustrate the effectiveness of the proposed methods.

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Subiantoro, Aries, F. Yusivar, B. Budiardjo und M. I. Al-Hamid. „Identification and Control Design of Fuzzy Takagi-Sugeno Model for Pressure Process Rig“. Advanced Materials Research 605-607 (Dezember 2012): 1810–18. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.1810.

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The design of an intelligent controller based on fuzzy TS model for a pressure process rig is presented. The proposed controller consists of a fuzzy TS model, a feedback fuzzy TS model, and a low pass filter combined in an internal model control structure. The identification of the fuzzy TS model uses fuzzy clustering technique to mimic the nonlinearity characteristic of the process. Instead of least-squares algorithm, the instrumental variable method is used to estimate the consequent parameters of the fuzzy TS model in order to avoid inconsistency problem. The identified model is validated with the performance indicators variance-accounted-for and root mean square. By using the technique of inverse fuzzy model analytically, the feedback fuzzy controller is designed based on the identified fuzzy TS model. The performance of the proposed controller is verified through experiments at various operating points.

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Kocian, Jiri, Stepan Ozana und Jiri Koziorek. „An Approach to Optimization of Takagi-Sugeno Type Fuzzy Regulator Parameters by Genetic Algorithm from Mamdani Regulation Surface“. Applied Mechanics and Materials 248 (Dezember 2012): 545–50. http://dx.doi.org/10.4028/www.scientific.net/amm.248.545.

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Many scientific papers deals with the usage of fuzzy rules to implement PID control. Fuzzy models, especially the Takagi-Sugeno-type, have received significant attention from various fields of interest. It is very often very difficult to determine all the parameters of the Takagi-Sugeno-type controller. In this paper we present optimization of Takagi-Sugeno-type fuzzy regulator parameters by genetic algorithm. Implementation of universal fuzzy P/PS/PD function block implemented to the PLC Simatic S7 300/400 is introduced. Mamdani model is used as comparative model. Parameters of Takagi-Sugeno-type fuzzy regulator are determined by genetic algorithm optimization from comparative regulation surface.

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Namazov, M., und A. Alili. „Stable and Optimal Controller Design for Takagi-Sugeno Fuzzy Model Based Control Systems via Linear Matrix Inequalities“. Information Technologies and Control 14, Nr. 3 (01.09.2016): 31–40. http://dx.doi.org/10.1515/itc-2017-0010.

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AbstractThis paper deals with a systematic design procedure that guarantees the stability and optimal performance of the nonlinear systems described by Takagi-Sugeno fuzzy models. Takagi-Sugeno fuzzy model allows us to represent a nonlinear system by linear models in different state space regions. The overall fuzzy model is obtained by fuzzy blending of these linear models. Then based on this model, linear controllers are designed for each linear model using parallel distributed compensation. Stability and optimal performance conditions for Takagi-Sugeno fuzzy control systems can be represented by a set of linear matrix inequalities which can be solved using software packages such as MATLAB’s LMI Toolbox. This design procedure is illustrated for a nonlinear system which is described by a two-rule Takagi-Sugeno fuzzy model. The fuzzy model was built in MATLAB Simulink and a code was written in LMI Toolbox to determine the controller gains subject to the proposed design approach.

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Hsiao, Feng-Hsiag, und Wei-Ling Chiang. „Application of Fuzzy H∞ Control via T–S Fuzzy Models for Nonlinear Time-Delay Systems“. International Journal on Artificial Intelligence Tools 12, Nr. 02 (Juni 2003): 117–37. http://dx.doi.org/10.1142/s0218213003001174.

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This paper deals with the problem of stability analysis and stabilization via Takagi-Sugeno (T-S) fuzzy models for nonlinear time-delay systems. First, Takagi-Sugeno (T-S) fuzzy models and some stability results are recalled. To design fuzzy controllers, nonlinear time-delay systems are represented by Takagi-Sugeno fuzzy models. The concept of parallel-distributed compensation (PDC) is employed to determine structures of fuzzy controllers from the T-S fuzzy models. LMI-based design problems are defined and employed to find feedback gains of fuzzy controller and common positive definite matrices P satisfying stability a delay-dependent stability criterion derived in terms of Lyapunov direct method. Based on the control scheme and this criterion, a fuzzy controller is then designed via the technique of PDC to stabilize the nonlinear time-delay system and the H∞ control performance is achieved in the mean time. Finally, the proposed controller design method is demonstrated through numerical simulations on the chaotic and resonant systems.

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Nelles, Oliver. „Structure Optimization of Takagi-Sugeno Fuzzy Models“. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 06, Nr. 02 (April 1998): 161–70. http://dx.doi.org/10.1142/s0218488598000148.

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A new approach for nonlinear system identification based on Takagi-Sugeno fuzzy models is presented. The premise structure and membership functions are optimized by the LOLIMOT (local linear model tree) algorithm, see [1]. This method is extended by a subset selection technique which automatically determines the structure of the local linear models in the rule consequents. This allows to select the significant input variables for static models and additionally the determination of the dynamic orders and dead times for dynamic models. The utilized subset selection technique is the orthogonal least-squares (OLS) algorithm. It exploits the linear regression structure of the problem and thus is very fast. The applicability of the proposed approach is illustrated by the identification of a transport delay process which has operating point dependent time constants and dead times.

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Marie Guerra, Thierry, und Laurent Vermeiren. „Control laws for Takagi–Sugeno fuzzy models“. Fuzzy Sets and Systems 120, Nr. 1 (Mai 2001): 95–108. http://dx.doi.org/10.1016/s0165-0114(99)00058-5.

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30

Johansen, T. A., und R. Babuska. „Multiobjective identification of Takagi-Sugeno fuzzy models“. IEEE Transactions on Fuzzy Systems 11, Nr. 6 (Dezember 2003): 847–60. http://dx.doi.org/10.1109/tfuzz.2003.819824.

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31

Chen, Shinn-Horng, Wen-Hsien Ho und Jyh-Horng Chou. „Robust Local Regularity and Controllability of Uncertain TS Fuzzy Descriptor Systems“. Journal of Applied Mathematics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/825416.

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The robust local regularity and controllability problem for the Takagi-Sugeno (TS) fuzzy descriptor systems is studied in this paper. Under the assumptions that the nominal TS fuzzy descriptor systems are locally regular and controllable, a sufficient criterion is proposed to preserve the assumed properties when the structured parameter uncertainties are added into the nominal TS fuzzy descriptor systems. The proposed sufficient criterion can provide the explicit relationship of the bounds on parameter uncertainties for preserving the assumed properties. An example is given to illustrate the application of the proposed sufficient condition.

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Enemegio, Rodolfo, Francisco Jurado und Jonathan Villanueva-Tavira. „Experimental Evaluation of a Takagi–Sugeno Fuzzy Controller for an EV3 Ballbot System“. Applied Sciences 14, Nr. 10 (12.05.2024): 4103. http://dx.doi.org/10.3390/app14104103.

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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.

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Chaubey, Shivam, und Vicenç Puig. „Autonomous Vehicle State Estimation and Mapping Using Takagi–Sugeno Modeling Approach“. Sensors 22, Nr. 9 (28.04.2022): 3399. http://dx.doi.org/10.3390/s22093399.

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This paper proposes an optimal approach for state estimation based on the Takagi–Sugeno (TS) Kalman filter using measurement sensors and rough pose obtained from LIDAR scan end-points matching. To obtain stable and optimal TS Kalman gain for estimator design, a linear matrix inequality (LMI) is optimized which is constructed from Lyapunov stability criteria and dual linear quadratic regulator (LQR). The technique utilizes a Takagi–Sugeno (TS) representation of the system, which allows modeling the complex nonlinear dynamics in such a way that linearization is not required for the estimator or controller design. In addition, the TS fuzzy representation is exploited to obtain a real-time Kalman gain, avoiding the expensive optimization of LMIs at every step. The estimation schema is integrated with a nonlinear model-predictive control (NMPC) that is in charge of controlling the vehicle. For the demonstration, the approach is tested in the simulation, and for practical validity, a small-scale autonomous car is used.

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Precup, Radu-Emil, Marius L. Tomescu und Stefan Preitl. „Lorenz System Stabilization Using Fuzzy Controllers“. International Journal of Computers Communications & Control 2, Nr. 3 (01.09.2007): 279. http://dx.doi.org/10.15837/ijccc.2007.3.2360.

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The paper suggests a Takagi Sugeno (TS) fuzzy logic controller (FLC) designed to stabilize the Lorentz chaotic systems. The stability analysis of the fuzzy control system is performed using Barbashin-Krasovskii theorem. This paper proves that if the derivative of Lyapunov function is negative semi-definite for each fuzzy rule then the controlled Lorentz system is asymptotically stable in the sense of Lyapunov. The stability theorem suggested here offers sufficient conditions for the stability of the Lorenz system controlled by TS FLCs. An illustrative example describes the application of the new stability analysis method.

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Jafari, Sadiqa, Zeinab Shahbazi und Yung-Cheol Byun. „Improving the Road and Traffic Control Prediction Based on Fuzzy Logic Approach in Multiple Intersections“. Mathematics 10, Nr. 16 (09.08.2022): 2832. http://dx.doi.org/10.3390/math10162832.

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Traffic congestion is a significant issue in many countries today. The suggested method is a novel control method based on multiple intersections considering the kind of traffic light and the duration of the green phase to determine the optimal balance at intersections by using fuzzy logic control, for which the balance should be adaptable to the unchanging behavior of time. It should reduce traffic volume in transport, average waits for each vehicle, and collisions between cars by controlling this balance in response to the typical behavior of time and randomness in traffic conditions. The proposed method is investigated at intersections using a sampling multi-agent system to set traffic light timings appropriately. The program is provided with many intersections, each of which is an independent entity exchanging information with the others. The stability per entity is proven separately. Simulation results show that Takagi–Sugeno (TS) fuzzy modeling performs better than Takagi–Sugeno (TS) fixed-time scheduling in decreasing the length of queueing times for vehicles.

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Zuo, Hua, Guangquan Zhang, Witold Pedrycz, Vahid Behbood und Jie Lu. „Fuzzy Regression Transfer Learning in Takagi–Sugeno Fuzzy Models“. IEEE Transactions on Fuzzy Systems 25, Nr. 6 (Dezember 2017): 1795–807. http://dx.doi.org/10.1109/tfuzz.2016.2633376.

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37

Jovanovic, Radisa, und Vladimir Zaric. „Identification and control of a heat flow system based on the Takagi-Sugeno fuzzy model using the grey wolf optimization algorithm“. Thermal Science, Nr. 00 (2021): 324. http://dx.doi.org/10.2298/tsci210825324j.

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Even though, it is mostly used by process control engineers, the temperature control remains an important task for researchers. This paper addressed two separate issues concerning model optimization and control. Firstly, the linear models for the three different operating points of the heat flow system were found. From these identified models a Takagi-Sugeno model is obtained using fixed membership functions in the premises of the rules. According to the chosen objective function, parameters in the premise part of Takagi-Sugeno fuzzy model were optimized using the grey wolf algorithm. Furthermore, by using the parallel distributed compensation a fuzzy controller is developed via the fuzzy blending of three proportional + sum controllers designed for each of the operating points. In order to evaluate performance, a comparison is made between the fuzzy controller and local linear controllers. Moreover, the fuzzy controllers from the optimized and initial Takagi-Sugeno plant models are compared. The experimental results on a heat flow platform are presented to validate efficiency of the proposed method.

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Jonnalagadda, Vimala Kumari, Vinodh Kumar Elumalai, Harvir Singh und Amit Prasad. „Nonlinear control design using Takagi-Sugeno fuzzy applied to under-actuated visual servo system“. Transactions of the Institute of Measurement and Control 42, Nr. 15 (10.07.2020): 2969–83. http://dx.doi.org/10.1177/0142331220936584.

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This paper presents the Takagi-Sugeno (TS) fuzzy control design for nonlinear stabilization and tracking control of a ball on plate system. To deal with the plant nonlinearity and the fuzzy convergence issue, we formulate the parallel distributed compensator (PDC) TS fuzzy model to characterize the global behaviour of the nonlinear system and synthesize a feasible control framework using a velocity compensation scheme. The nonlinear dynamics of the ball on plate system is obtained using the Euler-Lagrangian energy based approach. To identify the moving objects in the video stream, a background subtraction algorithm using thresholding technique is formulated. Moreover, the stability analysis of the TS fuzzy control is reduced to linear matrix inequality (LMI) problem and solved using the Lyapunov direct method. The potential benefits of the proposed control structure for real time test cases are experimentally assessed using hardware in loop (HIL) testing on a ball on plate system. Experimental results substantiate that the TS fuzzy scheme can significantly improve not only the tracking performance but also the robustness of the closed loop system.

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Liutkevičius, R. „Fuzzy Hammerstein Model of Nonlinear Plant“. Nonlinear Analysis: Modelling and Control 13, Nr. 2 (25.04.2008): 201–12. http://dx.doi.org/10.15388/na.2008.13.2.14580.

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This paper presents the synthesis and analysis of the enhanced predictive fuzzy Hammerstein model of the water tank system. Fuzzy Hammerstein model was compared with three other fuzzy models: the first was synthesized using Mamdani type rule base, the second – Takagi-Sugeno type rule base and the third – composed of Mamdani and Takagi-Sugeno rule bases. The synthesized model is invertible so it can be used in the model based control. The fuzzy Hammerstein model was synthesized to eliminate disadvantages of the other fuzzy models. The advantage of the fuzzy Hammerstein model was experimentally proved and presented in this paper.

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Precup, Radu-Emil, Stefan Preitl, Claudia-Adina Bojan-Dragos, Mircea-Bogdan Radac, Alexandra-Iulia Szedlak-Stinean, Elena-Lorena Hedrea und Raul-Cristian Roman. „AUTOMOTIVE APPLICATIONS OF EVOLVING TAKAGI-SUGENO-KANG FUZZY MODELS“. Facta Universitatis, Series: Mechanical Engineering 15, Nr. 2 (02.08.2017): 231. http://dx.doi.org/10.22190/fume170505011p.

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This paper presents theoretical and application results concerning the development of evolving Takagi-Sugeno-Kang fuzzy models for two dynamic systems, which will be viewed as controlled processes, in the field of automotive applications. The two dynamic systems models are nonlinear dynamics of the longitudinal slip in the Anti-lock Braking Systems (ABS) and the vehicle speed in vehicles with the Continuously Variable Transmission (CVT) systems. The evolving Takagi-Sugeno-Kang fuzzy models are obtained as discrete-time fuzzy models by incremental online identification algorithms. The fuzzy models are validated against experimental results in the case of the ABS and the first principles simulation results in the case of the vehicle with the CVT.

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Latrach, Chedia, Mourad Kchaou, Abdelhamid Rabhi und Ahmed El Hajjaji. „Decentralized networked control system design using Takagi-Sugeno (TS) fuzzy approach“. International Journal of Automation and Computing 12, Nr. 2 (April 2015): 125–33. http://dx.doi.org/10.1007/s11633-015-0879-9.

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42

Kukolj, Dragan. „Design of adaptive Takagi–Sugeno–Kang fuzzy models“. Applied Soft Computing 2, Nr. 2 (Dezember 2002): 89–103. http://dx.doi.org/10.1016/s1568-4946(02)00032-7.

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43

Angelov, Plamen, José Victor, António Dourado und Dimitar Filev. „On-Line Evolution of Takagi-Sugeno Fuzzy Models“. IFAC Proceedings Volumes 37, Nr. 16 (September 2004): 67–72. http://dx.doi.org/10.1016/s1474-6670(17)30852-2.

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Tencer, L., M. Reznakova und M. Cheriet. „TITS-FM: Transductive incremental Takagi-Sugeno fuzzy models“. Applied Soft Computing 26 (Januar 2015): 531–44. http://dx.doi.org/10.1016/j.asoc.2014.09.024.

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45

Kaewpraek, Nikorn, und Wudhichai Assawinchaichote. „Control of PMSG Wind Energy Conversion System with TS Fuzzy State-Feedback Controller“. Applied Mechanics and Materials 446-447 (November 2013): 728–32. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.728.

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This paper presents a Takagi-Sugeno (TS) fuzzy state-feedback controller based on a linear matrix inequality (LMI) approach for the permanent magnet synchronous generator of wind energy conversion system (PMSG-WECS). A dc/dc converter is considered to regulate the maximum power output of the system. To show its effectiveness, the dynamic model is replaced by the TS fuzzy model, which the proposed controller can be applied to the PMSG-WECS, while the controller gains can be obtained by solving set of a LMI approach. The proposed controller guarantees the stability of the system. Therefore, the performance of the proposed TS fuzzy state-feedback controller is assessed through the computer simulation.

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Chang, Wen-Jer, und Chein-Chung Sun. „Constrained fuzzy controller design of discrete Takagi–Sugeno fuzzy models“. Fuzzy Sets and Systems 133, Nr. 1 (Januar 2003): 37–55. http://dx.doi.org/10.1016/s0165-0114(02)00276-2.

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47

Zuo, Hua, Guangquan Zhang, Witold Pedrycz, Vahid Behbood und Jie Lu. „Granular Fuzzy Regression Domain Adaptation in Takagi–Sugeno Fuzzy Models“. IEEE Transactions on Fuzzy Systems 26, Nr. 2 (April 2018): 847–58. http://dx.doi.org/10.1109/tfuzz.2017.2694801.

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48

Yu, Fang-Ming. „The compact fuzzy filter design via Takagi–Sugeno fuzzy models“. Expert Systems with Applications 36, Nr. 3 (April 2009): 4412–16. http://dx.doi.org/10.1016/j.eswa.2008.05.036.

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49

Oke, Paul, und Sing Kiong Nguang. „Robust H∞ Takagi–Sugeno fuzzy output-feedback control for differential speed steering vehicles“. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, Nr. 12 (09.06.2020): 2822–35. http://dx.doi.org/10.1177/0954407020918705.

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This paper studied the modelling and control of four-wheel independently driven electric vehicles using differential speed steering. The Takagi–Sugeno fuzzy modelling approach represents the nonlinearities of the four-wheel independently driven electric vehicle state variables in several system models. The proposed controller design is a robust Takagi–Sugeno fuzzy output-feedback control based on a fuzzy Lyapunov function approach. More precisely, the Lyapunov function is chosen to be dependent on the membership functions. Sufficient conditions for the existence of the robust Takagi–Sugeno fuzzy controller are given in terms of linear matrix inequality constraints. The designed parameters are tested by simulating the four-wheel independently driven electric vehicles under varying operating conditions. The simulation results underscore the robustness and disturbance rejection importance of the proposed controller, which is then contrasted to better highlight the improved performance of the proposed approach over a fixed robust controller design.

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Abdelmalek, Ibtissem, Noureddine Goléa und Mohamed Hadjili. „A New Fuzzy Lyapunov Approach to Non-Quadratic Stabilization of Takagi-Sugeno Fuzzy Models“. International Journal of Applied Mathematics and Computer Science 17, Nr. 1 (01.03.2007): 39–51. http://dx.doi.org/10.2478/v10006-007-0005-4.

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A New Fuzzy Lyapunov Approach to Non-Quadratic Stabilization of Takagi-Sugeno Fuzzy ModelsIn this paper, new non-quadratic stability conditions are derived based on the parallel distributed compensation scheme to stabilize Takagi-Sugeno (T-S) fuzzy systems. We use a non-quadratic Lyapunov function as a fuzzy mixture of multiple quadratic Lyapunov functions. The quadratic Lyapunov functions share the same membership functions with the T-S fuzzy model. The stability conditions we propose are less conservative and stabilize also fuzzy systems which do not admit a quadratic stabilization. The proposed approach is based on two assumptions. The first one relates to a proportional relation between multiple Lyapunov functions and the second one considers an upper bound to the time derivative of the premise membership functions. To illustrate the advantages of our proposal, four examples are given.

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