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Journal articles on the topic 'Takagi-Sugeno fuzzy models'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Kocian, Jiri, Stepan Ozana, and 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 (December 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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2

Namazov, M., and A. Alili. "Stable and Optimal Controller Design for Takagi-Sugeno Fuzzy Model Based Control Systems via Linear Matrix Inequalities." Information Technologies and Control 14, no. 3 (September 1, 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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3

Hsiao, Feng-Hsiag, and 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, no. 02 (June 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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4

Nelles, Oliver. "Structure Optimization of Takagi-Sugeno Fuzzy Models." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 06, no. 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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5

Marie Guerra, Thierry, and Laurent Vermeiren. "Control laws for Takagi–Sugeno fuzzy models." Fuzzy Sets and Systems 120, no. 1 (May 2001): 95–108. http://dx.doi.org/10.1016/s0165-0114(99)00058-5.

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6

Johansen, T. A., and R. Babuska. "Multiobjective identification of Takagi-Sugeno fuzzy models." IEEE Transactions on Fuzzy Systems 11, no. 6 (December 2003): 847–60. http://dx.doi.org/10.1109/tfuzz.2003.819824.

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7

Zuo, Hua, Guangquan Zhang, Witold Pedrycz, Vahid Behbood, and Jie Lu. "Fuzzy Regression Transfer Learning in Takagi–Sugeno Fuzzy Models." IEEE Transactions on Fuzzy Systems 25, no. 6 (December 2017): 1795–807. http://dx.doi.org/10.1109/tfuzz.2016.2633376.

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8

Liutkevičius, R. "Fuzzy Hammerstein Model of Nonlinear Plant." Nonlinear Analysis: Modelling and Control 13, no. 2 (April 25, 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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9

Precup, Radu-Emil, Stefan Preitl, Claudia-Adina Bojan-Dragos, Mircea-Bogdan Radac, Alexandra-Iulia Szedlak-Stinean, Elena-Lorena Hedrea, and Raul-Cristian Roman. "AUTOMOTIVE APPLICATIONS OF EVOLVING TAKAGI-SUGENO-KANG FUZZY MODELS." Facta Universitatis, Series: Mechanical Engineering 15, no. 2 (August 2, 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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10

Kukolj, Dragan. "Design of adaptive Takagi–Sugeno–Kang fuzzy models." Applied Soft Computing 2, no. 2 (December 2002): 89–103. http://dx.doi.org/10.1016/s1568-4946(02)00032-7.

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11

Angelov, Plamen, José Victor, António Dourado, and Dimitar Filev. "On-Line Evolution of Takagi-Sugeno Fuzzy Models." IFAC Proceedings Volumes 37, no. 16 (September 2004): 67–72. http://dx.doi.org/10.1016/s1474-6670(17)30852-2.

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12

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

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13

Abdelmalek, Ibtissem, Noureddine Goléa, and 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, no. 1 (March 1, 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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14

Chang, Wen-Jer, and Chein-Chung Sun. "Constrained fuzzy controller design of discrete Takagi–Sugeno fuzzy models." Fuzzy Sets and Systems 133, no. 1 (January 2003): 37–55. http://dx.doi.org/10.1016/s0165-0114(02)00276-2.

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15

Zuo, Hua, Guangquan Zhang, Witold Pedrycz, Vahid Behbood, and Jie Lu. "Granular Fuzzy Regression Domain Adaptation in Takagi–Sugeno Fuzzy Models." IEEE Transactions on Fuzzy Systems 26, no. 2 (April 2018): 847–58. http://dx.doi.org/10.1109/tfuzz.2017.2694801.

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16

Yu, Fang-Ming. "The compact fuzzy filter design via Takagi–Sugeno fuzzy models." Expert Systems with Applications 36, no. 3 (April 2009): 4412–16. http://dx.doi.org/10.1016/j.eswa.2008.05.036.

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17

Oke, Paul, and 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, no. 12 (June 9, 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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18

Taniguchi, Tadanari, and Kazuo Tanaka. "Nonlinear Model Following Control via Takagi-Sugeno Fuzzy Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 2 (April 20, 1999): 68–74. http://dx.doi.org/10.20965/jaciii.1999.p0068.

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This paper presents a unified approach toward regulation and servocontrol problems as special cases of a nonlinear model following control via the Takagi-Sugeno fuzzy model. New parallel distributed compensation (PDC) is presented for realizing a nonlinear model following control. The new PDC fuzzy controller mirrors the structures of two Takagi-Sugeno fuzzy models representing a nonlinear system and nonlinear reference model. First, we derive linear matrix inequality (LMI) conditions to linearize the error system between the feedback system and the nonlinear reference model. A controller is designed using LMI conditions. Design examples verify the usefulness of nonlinear model following control.

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19

Roubos, J. A., S. Mollov, R. Babuška, and H. B. Verbruggen. "Fuzzy model-based predictive control using Takagi–Sugeno models." International Journal of Approximate Reasoning 22, no. 1-2 (September 1999): 3–30. http://dx.doi.org/10.1016/s0888-613x(99)00020-1.

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20

González-Mendoza, Miguel, Neil Hernández-Gress, and André Titli. "SVM clustering for identification of takagi-sugeno fuzzy models." IFAC Proceedings Volumes 36, no. 12 (July 2003): 209–14. http://dx.doi.org/10.1016/s1474-6670(17)32537-5.

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21

Chang, Chia-Wen, and Chin-Wang Tao. "A Novel Approach to Implement Takagi-Sugeno Fuzzy Models." IEEE Transactions on Cybernetics 47, no. 9 (September 2017): 2353–61. http://dx.doi.org/10.1109/tcyb.2017.2701900.

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22

Páramo-Carranza, L. A., J. A. Meda-Campaña, José de Jesús Rubio, R. Tapia-Herrera, A. V. Curtidor-López, A. Grande-Meza, and I. Cázares-Ramírez. "Discrete-time Kalman filter for Takagi–Sugeno fuzzy models." Evolving Systems 8, no. 3 (April 11, 2017): 211–19. http://dx.doi.org/10.1007/s12530-017-9181-0.

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23

Nowaková, Jana, Miroslav Pokorný, and Martin Pieš. "Conventional controller design based on Takagi–Sugeno fuzzy models." Journal of Applied Logic 13, no. 2 (June 2015): 148–55. http://dx.doi.org/10.1016/j.jal.2014.11.008.

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24

Aydi, Amira, Mohamed Djemel, and Mohamed Chtourou. "Two fuzzy internal model control methods for nonlinear uncertain systems." International Journal of Intelligent Computing and Cybernetics 10, no. 2 (June 12, 2017): 223–40. http://dx.doi.org/10.1108/ijicc-07-2016-0026.

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Purpose The purpose of this paper is to use the internal model control to deal with nonlinear stable systems affected by parametric uncertainties. Design/methodology/approach The dynamics of a considered system are approximated by a Takagi-Sugeno fuzzy model. The parameters of the fuzzy rules premises are determined manually. However, the parameters of the fuzzy rules conclusions are updated using the descent gradient method under inequality constraints in order to ensure the stability of each local model. In fact, without making these constraints the training algorithm can procure one or several unstable local models even if the desired accuracy in the training step is achieved. The considered robust control approach is the internal model. It is synthesized based on the Takagi-Sugeno fuzzy model. Two control strategies are considered. The first one is based on the parallel distribution compensation principle. It consists in associating an internal model control for each local model. However, for the second strategy, the control law is computed based on the global Takagi-Sugeno fuzzy model. Findings According to the simulation results, the stability of all local models is obtained and the proposed fuzzy internal model control approaches ensure robustness against parametric uncertainties. Originality/value This paper introduces a method for the identification of fuzzy model parameters ensuring the stability of all local models. Using the resulting fuzzy model, two fuzzy internal model control designs are presented.

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25

Alshammari, Badr, Rim Ben Salah, Omar Kahouli, and Lioua Kolsi. "Design of Fuzzy TS-PDC Controller for Electrical Power System via Rules Reduction Approach." Symmetry 12, no. 12 (December 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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26

Wang, Ning, Wen Yao, Yong Zhao, and Xiaoqian Chen. "Bayesian calibration of computer models based on Takagi–Sugeno fuzzy models." Computer Methods in Applied Mechanics and Engineering 378 (May 2021): 113724. http://dx.doi.org/10.1016/j.cma.2021.113724.

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27

Patel, Himanshukumar R., and Vipul A. Shah. "Stable Fault Tolerant Controller Design for Takagi–Sugeno Fuzzy Model-Based Control Systems via Linear Matrix Inequalities: Three Conical Tank Case Study." Energies 12, no. 11 (June 11, 2019): 2221. http://dx.doi.org/10.3390/en12112221.

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This paper deals with a methodical design approach of fault-tolerant controller that gives assurance for the the stabilization and acceptable control performance of the nonlinear systems which can be described by Takagi–Sugeno (T–S) fuzzy models. Takagi–Sugeno fuzzy model gives a unique edge that allows us to apply the traditional linear system theory for the investigation and blend of nonlinear systems by linear models in a different state space region. The overall fuzzy model of the nonlinear system is obtained by fuzzy combination of the all linear models. After that, based on this linear model, we employ parallel distributed compensation for designing linear controllers for each linear model. Also this paper reports of the T–S fuzzy system with less conservative stabilization condition which gives decent performance. However, the controller synthesis for nonlinear systems described by the T–S fuzzy model is a complicated task, which can be reduced to convex problems linking with linear matrix inequalities (LMIs). Further sufficient conservative stabilization conditions are represented by a set of LMIs for the Takagi–Sugeno fuzzy control systems, which can be solved by using MATLAB software. Two-rule T–S fuzzy model is used to describe the nonlinear system and this system demonstrated with proposed fault-tolerant control scheme. The proposed fault-tolerant controller implemented and validated on three interconnected conical tank system with two constraints in terms of faults, one issed to build the actuator and sond is system component (leak) respectively. The MATLAB Simulink platform with linear fuzzy models and an LMI Toolbox was used to solve the LMIs and determine the controller gains subject to the proposed design approach.

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28

Yershov, S. V., and R. М. Ponomarenko. "Methods of parallel computing for multilevel fuzzy Takagi – Sugeno systems." PROBLEMS IN PROGRAMMING, no. 2-3 (June 2016): 141–49. http://dx.doi.org/10.15407/pp2016.02-03.141.

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Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models is carried out.

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29

Jacquin, A. P., and A. Y. Shamseldin. "Sensitivity analysis of Takagi-Sugeno-Kang rainfall-runoff fuzzy models." Hydrology and Earth System Sciences 13, no. 1 (January 23, 2009): 41–55. http://dx.doi.org/10.5194/hess-13-41-2009.

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Abstract. This paper is concerned with the sensitivity analysis of the model parameters of the Takagi-Sugeno-Kang fuzzy rainfall-runoff models previously developed by the authors. These models are classified in two types of fuzzy models, where the first type is intended to account for the effect of changes in catchment wetness and the second type incorporates seasonality as a source of non-linearity. The sensitivity analysis is performed using two global sensitivity analysis methods, namely Regional Sensitivity Analysis and Sobol's variance decomposition. The data of six catchments from different geographical locations and sizes are used in the sensitivity analysis. The sensitivity of the model parameters is analysed in terms of several measures of goodness of fit, assessing the model performance from different points of view. These measures include the Nash-Sutcliffe criteria, volumetric errors and peak errors. The results show that the sensitivity of the model parameters depends on both the catchment type and the measure used to assess the model performance.

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30

Jacquin, A. P., and A. Y. Shamseldin. "Sensitivity analysis of Takagi-Sugeno-Kang rainfall-runoff\\ fuzzy models." Hydrology and Earth System Sciences Discussions 5, no. 4 (July 18, 2008): 1967–2003. http://dx.doi.org/10.5194/hessd-5-1967-2008.

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Abstract. This paper is concerned with the sensitivity analysis of the model parameters of the Takagi-Sugeno-Kang fuzzy rainfall-runoff models previously developed by the authors. These models are classified in two types of fuzzy models, where the first type is intended to account for the effect of changes in catchment wetness and the second type incorporates seasonality as a source of non-linearity. The sensitivity analysis is performed using two global sensitivity analysis methods, namely Regional Sensitivity Analysis and Sobol's variance decomposition. The data of six catchments from different geographical locations and sizes are used in the sensitivity analysis. The sensitivity of the model parameters is analysed in terms of several measures of goodness of fit, assessing the model performance from different points of view. These measures include the Nash-Sutcliffe criteria, volumetric errors and peak errors. The results show that the sensitivity of the model parameters depends on both the catchment type and the measure used to assess the model performance.

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31

Abonyi, J., R. Babuska, and F. Szeifert. "Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, no. 5 (October 2002): 612–21. http://dx.doi.org/10.1109/tsmcb.2002.1033180.

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Park, Chang-Woo, Chang-Ho Hyun, and Mignon Park. "On-line Parameter Estimator Based on Takagi-Sugeno Fuzzy Models." Journal of Korean Institute of Intelligent Systems 12, no. 5 (October 1, 2002): 481–86. http://dx.doi.org/10.5391/jkiis.2002.12.5.481.

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33

Joongseon Joh, Ye-Haw Chen, and R. Langari. "On the stability issues of linear Takagi-Sugeno fuzzy models." IEEE Transactions on Fuzzy Systems 6, no. 3 (1998): 402–10. http://dx.doi.org/10.1109/91.705508.

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Araya, Juan, and Aldo Cipriano. "OPTIMAL IDENTIFICATION OF TAKAGI-SUGENO FUZZY MODELS FOR NONLINEAR FDI." IFAC Proceedings Volumes 39, no. 13 (2006): 759–64. http://dx.doi.org/10.3182/20060829-4-cn-2909.00126.

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Delmotte, François, and Thierry Marie Guerra. "DISCRETE TAKAGI-SUGENO FUZZY MODELS: REDUCED NUMBER OF STABILIZATION CONDITIONS." IFAC Proceedings Volumes 38, no. 1 (2005): 413–18. http://dx.doi.org/10.3182/20050703-6-cz-1902.01147.

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Angelov, P. P., and D. P. Filev. "An Approach to Online Identification of Takagi-Sugeno Fuzzy Models." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 1 (February 2004): 484–98. http://dx.doi.org/10.1109/tsmcb.2003.817053.

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Klug, Michael, Eugênio B. Castelan, Valter J. S. Leite, and Luís F. P. Silva. "Fuzzy dynamic output feedback control through nonlinear Takagi–Sugeno models." Fuzzy Sets and Systems 263 (March 2015): 92–111. http://dx.doi.org/10.1016/j.fss.2014.05.019.

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Zhang, Sunjie, Zidong Wang, Jun Hu, Jinling Liang, and Fuad E. Alsaadi. "Analysis, Filtering, and Control for Takagi-Sugeno Fuzzy Models in Networked Systems." Abstract and Applied Analysis 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/856390.

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The fuzzy logic theory has been proven to be effective in dealing with various nonlinear systems and has a great success in industry applications. Among different kinds of models for fuzzy systems, the so-called Takagi-Sugeno (T-S) fuzzy model has been quite popular due to its convenient and simple dynamic structure as well as its capability of approximating any smooth nonlinear function to any specified accuracy within any compact set. In terms of such a model, the performance analysis and the design of controllers and filters play important roles in the research of fuzzy systems. In this paper, we aim to survey some recent advances on the T-S fuzzy control and filtering problems with various network-induced phenomena. The network-induced phenomena under consideration mainly include communication delays, packet dropouts, signal quantization, and randomly occurring uncertainties (ROUs). With such network-induced phenomena, the developments on T-S fuzzy control and filtering issues are reviewed in detail. In addition, some latest results on this topic are highlighted. In the end, conclusions are drawn and some possible future research directions are pointed out.

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Al-Gallaf, E. A. "Takagi-Sugeno Neuro-Fuzzy Modeling of a Multivariable Nonlinear Antenna System." Journal of Engineering Research [TJER] 2, no. 1 (December 1, 2005): 12. http://dx.doi.org/10.24200/tjer.vol2iss1pp12-24.

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This article investigates the use of a clustered based neuro-fuzzy system to nonlinear dynamic system modeling. It is focused on the modeling via Takagi-Sugeno (T-S) modeling procedure and the employment of fuzzy clustering to generate suitable initial membership functions. The T-S fuzzy modeling has been applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs. Compared to other well-known approximation techniques such as artificial neural networks, the employed neuro-fuzzy system has provided a more transparent representation of the nonlinear antenna system under study, mainly due to the possible linguistic interpretation in the form of rules. Created initial memberships are then employed to construct suitable T-S models. Furthermore, the T-S fuzzy models have been validated and checked through the use of some standard model validation techniques (like the correlation functions). This intelligent modeling scheme is very useful once making complicated systems linguistically transparent in terms of the fuzzy if-then rules.

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40

Zhang, Bin, and Yung C. Shin. "A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems." Applied Sciences 11, no. 1 (December 23, 2020): 62. http://dx.doi.org/10.3390/app11010062.

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A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy models can closely approximate the original unknown nonlinear system with a reduced number of linearizations. Based on T-S fuzzy models, optimal controllers are designed and implemented for a nonlinear two-link flexible joint robot, which demonstrates the possibility of implementing the well-established model-based optimal control method onto unknown nonlinear dynamic systems.

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Park, C. W., and Y. W. Cho. "Robust fuzzy feedback linearisation controllers for Takagi-Sugeno fuzzy models with parametric uncertainties." IET Control Theory & Applications 1, no. 5 (September 1, 2007): 1242–54. http://dx.doi.org/10.1049/iet-cta:20060265.

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Chang, Wen-Jer, Wei-Han Huang, Wei Chang, and Cheung-Chieh Ku. "Robust fuzzy control for continuous perturbed time-delay affine takagi-sugeno fuzzy models." Asian Journal of Control 13, no. 6 (May 24, 2011): 818–30. http://dx.doi.org/10.1002/asjc.401.

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43

Chang, Wen-Jer, Wei-Han Huang, and Cheung-Chieh Ku. "Robust fuzzy control for discrete perturbed time-delay affine Takagi-Sugeno fuzzy models." International Journal of Control, Automation and Systems 9, no. 1 (February 2011): 86–97. http://dx.doi.org/10.1007/s12555-011-0111-9.

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CHEN, CHEN-YUAN, JOHN RONG-CHUNG HSU, and CHENG-WU CHEN. "FUZZY LOGIC DERIVATION OF NEURAL NETWORK MODELS WITH TIME DELAYS IN SUBSYSTEMS." International Journal on Artificial Intelligence Tools 14, no. 06 (December 2005): 967–74. http://dx.doi.org/10.1142/s021821300500248x.

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Abstract:

This paper extends the Takagi-Sugeno (T-S) fuzzy model representation to analyze the stability of interconnected systems in which there exist time delays in subsystems. A novel stability criterion which can be solved numerically is presented in terms of Lyapunov's theory for fuzzy interconnected models. In this paper, we use linear difference inclusion (LDI) state-space representation to represent the fuzzy model. Then, the linear matrix inequality (LMI) optimization algorithm is employed to find common solution and then guarantee the asymptotic stability.

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Passaquay, D., S. Boverie, and A. Titli. "Comparison of Two Control Approaches Based on Fuzzy Takagi-Sugeno Models." IFAC Proceedings Volumes 33, no. 25 (September 2000): 89–94. http://dx.doi.org/10.1016/s1474-6670(17)39321-7.

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Mollov, S., R. Babuska, and H. B. Verbruggen. "Predictive Control by Multiple-Step Linearization of Takagi-Sugeno Fuzzy Models." IFAC Proceedings Volumes 31, no. 29 (October 1998): 77–78. http://dx.doi.org/10.1016/s1474-6670(17)38361-1.

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Pal, N. R., and S. Saha. "Simultaneous Structure Identification and Fuzzy Rule Generation for Takagi–Sugeno Models." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38, no. 6 (December 2008): 1626–38. http://dx.doi.org/10.1109/tsmcb.2008.2006367.

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Machado, J. B., R. J. G. B. Campello, and W. C. Amaral. "Takagi–Sugeno Fuzzy Models in the Framework of Orthonormal Basis Functions." IEEE Transactions on Cybernetics 43, no. 3 (June 2013): 858–70. http://dx.doi.org/10.1109/tsmcb.2012.2217323.

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Johansen, T. A., R. Shorten, and R. Murray-Smith. "On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models." IEEE Transactions on Fuzzy Systems 8, no. 3 (June 2000): 297–313. http://dx.doi.org/10.1109/91.855918.

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Sun, Fuchun, Huaping Liu, and Zengqi Sun. "Comments on “Constrained controller design of discrete Takagi–Sugeno fuzzy models”." Fuzzy Sets and Systems 146, no. 3 (September 2004): 473–76. http://dx.doi.org/10.1016/j.fss.2004.02.012.

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