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Journal articles on the topic 'Takagi-Sugeno modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Männle, Manfred. "FTSM — Fast Takagi-Sugeno Fuzzy Modeling." IFAC Proceedings Volumes 33, no. 11 (2000): 651–56. http://dx.doi.org/10.1016/s1474-6670(17)37434-7.

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2

Shao, Xuejuan, Jinggang Zhang, Xueliang Zhang, Zhicheng Zhao, and Zhimei Chen. "A novel anti-swing and position control method for overhead crane." Science Progress 103, no. 1 (2019): 003685041988353. http://dx.doi.org/10.1177/0036850419883539.

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Based on Takagi–Sugeno fuzzy modeling and linear matrix inequality with decay rate, this article presents a novel anti-swing and position control scheme for overhead cranes. First, the simplified nonlinear dynamic model is proposed by adopting a virtual control variable method to reduce the number of nonlinear terms. Then, the Takagi–Sugeno fuzzy model is constructed using sector nonlinear technique, and the anti-swing and position controller of overhead crane is designed based on a linear matrix inequality with decay rate. Finally, the proposed control method is compared with the traditional

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3

Sriraman, R., R. Samidurai, V. C. Amritha, G. Rachakit, and Prasanalakshmi Balaji. "System decomposition-based stability criteria for Takagi-Sugeno fuzzy uncertain stochastic delayed neural networks in quaternion field." AIMS Mathematics 8, no. 5 (2023): 11589–616. http://dx.doi.org/10.3934/math.2023587.

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<abstract><p>Stochastic disturbances often occur in real-world systems which can lead to undesirable system dynamics. Therefore, it is necessary to investigate stochastic disturbances in neural network modeling. As such, this paper examines the stability problem for Takagi-Sugeno fuzzy uncertain quaternion-valued stochastic neural networks. By applying Takagi-Sugeno fuzzy models and stochastic analysis, we first consider a general form of Takagi-Sugeno fuzzy uncertain quaternion-valued stochastic neural networks with time-varying delays. Then, by constructing suitable Lyapunov-Kras

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4

Keshwani, Deepak R., David D. Jones, and Rhonda M. Brand. "Review: Takagi–Sugeno Fuzzy Modeling of Skin Permeability." Cutaneous and Ocular Toxicology 24, no. 3 (2005): 149–63. http://dx.doi.org/10.1080/15569520500278690.

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5

Hadjili, M. L., and V. Wertz. "Takagi-Sugeno fuzzy modeling incorporating input variables selection." IEEE Transactions on Fuzzy Systems 10, no. 6 (2002): 728–42. http://dx.doi.org/10.1109/tfuzz.2002.805897.

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6

Salgado, Catia M., Joaquim L. Viegas, Carlos S. Azevedo, Marta C. Ferreira, Susana M. Vieira, and Joao M. C. Sousa. "Takagi–Sugeno Fuzzy Modeling Using Mixed Fuzzy Clustering." IEEE Transactions on Fuzzy Systems 25, no. 6 (2017): 1417–29. http://dx.doi.org/10.1109/tfuzz.2016.2639565.

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7

Rezaee, Babak. "Desulfurization process using Takagi–Sugeno–Kang fuzzy modeling." International Journal of Advanced Manufacturing Technology 46, no. 1-4 (2009): 191–97. http://dx.doi.org/10.1007/s00170-009-2031-x.

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8

Kim, Euntai, Heejin Lee, Chang-Hoon Lee, and Jung-Hwan Kim. "Moving Genetic Algorithm Based Fuzzy Modeling." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 4 (1999): 320–25. http://dx.doi.org/10.20965/jaciii.1999.p0320.

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We propose an approach to Takagi-Sugeno fuzzy modeling via a genetic algorithm consisting of 2 tuning steps - coarse and fine. A moving genetic algorithm (MGA) is proposed and used for fine tuning to obtain robust modeling results. Simulation results demonstrate the algorithm’s validity.

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9

Wang, Jin, Longze Hu, Fuyang Chen, and Changyun Wen. "Multiple-step fault estimation for interval type-II T-S fuzzy system of hypersonic vehicle with time-varying elevator faults." International Journal of Advanced Robotic Systems 14, no. 2 (2017): 172988141769914. http://dx.doi.org/10.1177/1729881417699149.

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This article proposes a multiple-step fault estimation algorithm for hypersonic flight vehicles that uses an interval type-II Takagi–Sugeno fuzzy model. An interval type-II Takagi–Sugeno fuzzy model is developed to approximate the nonlinear dynamic system and handle the parameter uncertainties of hypersonic firstly. Then, a multiple-step time-varying additive fault estimation algorithm is designed to estimate time-varying additive elevator fault of hypersonic flight vehicles. Finally, the simulation is conducted in both aspects of modeling and fault estimation; the validity and availability of

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10

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 (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 pr

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11

Allouche, Benyamine, Antoine Dequidt, Laurent Vermeiren, and Michel Dambrine. "Modeling and PDC fuzzy control of planar parallel robot." International Journal of Advanced Robotic Systems 14, no. 1 (2017): 172988141668711. http://dx.doi.org/10.1177/1729881416687112.

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Many works in the literature have studied the kinematical and dynamical issues of parallel robots. But it is still difficult to extend the vast control strategies to parallel mechanisms due to the complexity of the model-based control. This complexity is mainly caused by the presence of multiple closed kinematic chains, making the system naturally described by a set of differential–algebraic equations. The aim of this work is to control a two-degree-of-freedom parallel manipulator. A mechanical model based on differential–algebraic equations is given. The goal is to use the structural characte

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12

Robles, Ruben, Antonio Sala, Miguel Bernal, and Temoatzin Gonzalez. "Subspace-Based Takagi–Sugeno Modeling for Improved LMI Performance." IEEE Transactions on Fuzzy Systems 25, no. 4 (2017): 754–67. http://dx.doi.org/10.1109/tfuzz.2016.2574927.

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13

Bouzbida, Mohamed, Lassad Hassine, and Abdelkader Chaari. "Robust Kernel Clustering Algorithm for Nonlinear System Identification." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2427309.

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In engineering field, it is necessary to know the model of the real nonlinear systems to ensure its control and supervision; in this context, fuzzy modeling and especially the Takagi-Sugeno fuzzy model has drawn the attention of several researchers in recent decades owing to their potential to approximate nonlinear behavior. To identify the parameters of Takagi-Sugeno fuzzy model several clustering algorithms are developed such as the Fuzzy C-Means (FCM) algorithm, Possibilistic C-Means (PCM) algorithm, and Possibilistic Fuzzy C-Means (PFCM) algorithm. This paper presents a new clustering algo

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14

Chaubey, Shivam, and Vicenç Puig. "Autonomous Vehicle State Estimation and Mapping Using Takagi–Sugeno Modeling Approach." Sensors 22, no. 9 (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

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15

Haj Hamad, I., A. Chouchaine, and H. Bouzaouache. "A Takagi-Sugeno Fuzzy Model for Greenhouse Climate." Engineering, Technology & Applied Science Research 11, no. 4 (2021): 7424–29. http://dx.doi.org/10.48084/etasr.4291.

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This paper investigates the identification and modeling of a greenhouse's climate using real climate data from a greenhouse installed in the LAPER laboratory in Tunisia. The objective of this paper is to propose a solution to the problem of nonlinear time-variant inputs and outputs of greenhouse internal climate. Combining fuzzy logic technique with Least Mean Squares (LMS), a robust greenhouse climate model for internal temperature prediction is proposed. The simulation results demonstrate the effectiveness of the identification approach and the power of the implemented Takagi-Sugeno Fuzzy mo

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16

Zhao, Geng Ming, Tao Feng, and Li Qian Liang. "Automated Implementation from Modeling to FPGA of Vehicle Autopilot." Applied Mechanics and Materials 528 (February 2014): 339–45. http://dx.doi.org/10.4028/www.scientific.net/amm.528.339.

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This paper demonstrates a new strategy for accelerating FPGA realization which is integrated design from model to automated HDL code generation, and it’s applied to a design of Takagi-Sugeno fuzzy logic control (FLC) Systems on vehicle autopilot based on FPGA. The system designed by this strategy accurately guides vehicle to the destination by controlling size and angle of speed.

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17

Ji, Rui, Yupu Yang, and Weidong Zhang. "Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling." Neurocomputing 123 (January 2014): 281–91. http://dx.doi.org/10.1016/j.neucom.2013.07.017.

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18

Lohani, A. K., N. K. Goel, and K. K. S. Bhatia. "Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship." Journal of Hydrology 331, no. 1-2 (2006): 146–60. http://dx.doi.org/10.1016/j.jhydrol.2006.05.007.

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19

Rezaee, Babak, and M. H. Fazel Zarandi. "Data-driven fuzzy modeling for Takagi–Sugeno–Kang fuzzy system." Information Sciences 180, no. 2 (2010): 241–55. http://dx.doi.org/10.1016/j.ins.2009.08.021.

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20

Rezaee, Babak, Mohammad Hossein Fazel Zarandi, and Ismail Burhan Turksen. "Erratum to: Desulfurization process using Takagi–Sugeno–Kang fuzzy modeling." International Journal of Advanced Manufacturing Technology 46, no. 1-4 (2009): 199. http://dx.doi.org/10.1007/s00170-009-2350-y.

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21

Al-Gallaf, E. A. "Takagi-Sugeno Neuro-Fuzzy Modeling of a Multivariable Nonlinear Antenna System." Journal of Engineering Research [TJER] 2, no. 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 und

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22

Wittich, Felix, Lars Kistner, Andreas Kroll, Christopher Schott, and Thomas Niendorf. "On data-driven nonlinear uncertainty modeling: Methods and application for control-oriented surface condition prediction in hard turning." tm - Technisches Messen 87, no. 11 (2020): 732–41. http://dx.doi.org/10.1515/teme-2020-0057.

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AbstractIn this article, two data-driven modeling approaches are investigated, which allow an explicit modeling of uncertainty. For this purpose, parametric Takagi-Sugeno multi-models with bounded-error parameter estimation and nonparametric Gaussian process regression are applied and compared. These models can for instance be used for robust model-based control design. As an application, the prediction of residual stresses during hard turning depending on the machining parameters and the initial hardness is considered.

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23

Jafari, Sadiqa, Zeinab Shahbazi, and Yung-Cheol Byun. "Improving the Road and Traffic Control Prediction Based on Fuzzy Logic Approach in Multiple Intersections." Mathematics 10, no. 16 (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 pr

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24

Jingzhuo, Shi, Lv Lin, and Zhang Yu. "Dynamic Takagi-Sugeno Model for the Control of Ultrasonic Motor." Journal of Control Science and Engineering 2011 (2011): 1–9. http://dx.doi.org/10.1155/2011/219278.

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Model of ultrasonic motor is the foundation of the design of ultrasonic motor's speed and position controller. A two-input and one-output dynamic Takagi-Sugeno model of ultrasonic motor driving system is worked out using fuzzy reasoning modeling method in this paper. Many fuzzy reasoning modeling methods are sensitive to the initial values and easy to fall into local minimum, and have a large amount of calculation. In order to overcome these defects, equalized universe method is used in this paper to get clusters centers and obtain fuzzy clustering membership functions, and then, the unknown p

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25

ShuiLi Chen, Yuan Fang, and YunDong Wu. "A New Hybrid Fuzzy Clustering Approach to Takagi-Sugeno Fuzzy Modeling." International Journal of Digital Content Technology and its Applications 6, no. 18 (2012): 341–48. http://dx.doi.org/10.4156/jdcta.vol6.issue18.41.

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26

Malhotra, Neha, and Manju Bala. "Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion." Computers, Materials & Continua 71, no. 1 (2022): 1011–24. http://dx.doi.org/10.32604/cmc.2022.022451.

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27

Lee, Sang-Yun, Mignon Park, and Jaeho Baek. "Modeling of Dynamic Hysteresis Based on Takagi-Sugeno Fuzzy Duhem Model." International Journal of Fuzzy Logic and Intelligent Systems 13, no. 4 (2013): 277–83. http://dx.doi.org/10.5391/ijfis.2013.13.4.277.

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28

Muhammad, Mustapha, Salinda Buyamin, Mohamad N. Ahmad, and Sophan W. Nawawi. "Takagi-Sugeno fuzzy modeling of a two-wheeled inverted pendulum robot." Journal of Intelligent & Fuzzy Systems 25, no. 3 (2013): 535–46. http://dx.doi.org/10.3233/ifs-120658.

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29

Vasegh, Nastaran, and Farhad Khellat. "Takagi-Sugeno fuzzy modeling and chaos control of partial differential systems." Chaos: An Interdisciplinary Journal of Nonlinear Science 23, no. 4 (2013): 042101. http://dx.doi.org/10.1063/1.4823993.

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30

El hamdaouy, Achour, Issam Salhi, Abdellatif Belattar, and Said Doubabi. "Takagi–Sugeno fuzzy modeling for three-phase micro hydropower plant prototype." International Journal of Hydrogen Energy 42, no. 28 (2017): 17782–92. http://dx.doi.org/10.1016/j.ijhydene.2017.02.167.

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31

Kang, Jin-Shig. "Fuzzy Descriptor System Modeling and Control of Lagrange Dynamics with Regional Pole-Placement Constraint." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (2004): 362–68. http://dx.doi.org/10.20965/jaciii.2004.p0362.

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We present a descriptor fuzzy model for Lagrange dynamics and a controller design algorithm based on state feedback pole placement. The fuzzy descriptor system (FDS) model is a simple extension of the original Takagi-Sugeno fuzzy model for which a new controller is designed based on the linear matrix inequality (LMI) theory. We show that LMI-based regional pole-placement design for the FDS is easily solved. Two examples explain the controller’s simplicity and easy design.

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32

Boata, Remus. "Modeling of Daily Global Solar Irradiation in Timisoara by Using a Fuzzy Approach." Annals of West University of Timisoara - Physics 60, no. 1 (2018): 38–44. http://dx.doi.org/10.2478/awutp-2018-0004.

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AbstractThis paper proposes a new simple model to forecast daily global solar irradiation one day ahead using the Takagi-Sugeno fuzzy methods. The model is based on solar radiation data measured in Timisoara, Romania. The daily clearness index represents the direct variable used by the fuzzy algorithm. The model forecasts the clearness index at the moment of time t on basis of two previous values measured at time t-1 and t-2. An assessment of the model accuracy is performed.

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33

Gotz, Joelton Deonei, Mario Henrique Bigai, Gabriel Harteman, et al. "Design of a Takagi–Sugeno Fuzzy Exact Modeling of a Buck–Boost Converter." Designs 7, no. 3 (2023): 63. http://dx.doi.org/10.3390/designs7030063.

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DC–DC converters are used in many power electronics applications, such as switching power supply design, photovoltaic, power management systems, and electric and hybrid vehicles. Traditionally, DC–DC converters are linearly modeled using a typical operating point for their control design. Some recent works use nonlinear models for DC–DC converters, due to the inherent nonlinearity of the switching process. In this sense, a standout modeling technique is the Takagi–Sugeno fuzzy exact method due to its ability to represent nonlinear systems over the entire operating range. It is more faithful to

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34

Chatterjee, Amitava, and Keigo Watanabe. "An optimized Takagi-Sugeno type neuro-fuzzy system for modeling robot manipulators." Neural Computing and Applications 15, no. 1 (2005): 55–61. http://dx.doi.org/10.1007/s00521-005-0008-8.

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35

Kwong, C. K., K. Y. Chan, and H. Wong. "Takagi–Sugeno neural fuzzy modeling approach to fluid dispensing for electronic packaging." Expert Systems with Applications 34, no. 3 (2008): 2111–19. http://dx.doi.org/10.1016/j.eswa.2007.02.035.

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36

Flores-Hernández, A. A., J. Reyes-Reyes, C. M. Astorga-Zaragoza, G. L. Osorio-Gordillo, and C. D. García-Beltrán. "Temperature control of an alcoholic fermentation process through the Takagi–Sugeno modeling." Chemical Engineering Research and Design 140 (December 2018): 320–30. http://dx.doi.org/10.1016/j.cherd.2018.10.021.

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37

HUNT, K. J., R. HAAS, and M. BROWN. "ON THE FUNCTIONAL EQUIVALENCE OF FUZZY INFERENCE SYSTEMS AND SPLINE-BASED NETWORKS." International Journal of Neural Systems 06, no. 02 (1995): 171–84. http://dx.doi.org/10.1142/s0129065795000135.

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The conditions under which spline-based networks are functionally equivalent to the Takagi-Sugeno-model of fuzzy inference are formally established. We consider a generalized form of basis function network whose basis functions are splines. The result admits a wide range of fuzzy membership functions which are commonly encountered in fuzzy systems design. We use the theoretical background of functional equivalence to develop a hybrid fuzzy-spline net for inverse dynamic modeling of a hydraulically driven robot manipulator.

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38

Thomas, Robert, Usman T. Khan, Caterina Valeo, and Fatima Talebzadeh. "An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data." Environments 8, no. 6 (2021): 50. http://dx.doi.org/10.3390/environments8060050.

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Fuzzy set theory has shown potential for reducing uncertainty as a result of data sparsity and also provides advantages for quantifying gradational changes like those of pollutant concentrations through fuzzy clustering based approaches. The ability to lower the sampling frequency and perform laboratory analyses on fewer samples, yet still produce an adequate pollutant distribution map, would reduce the initial cost of new remediation projects. To assess the ability of fuzzy modeling to make spatial predictions using fewer sample points, its predictive ability was compared with the ordinary kr

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39

Liu, Ling, Bao Guo Tang, and 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 requ

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40

Kulikova, Irina V. "MODELING THE SYNTHESIS OF TAKAGI — SUGENO — KANG FUZZY CONTROLLERS IN SOME CONTROL SYSTEMS." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 7, no. 2 (2021): 147–69. http://dx.doi.org/10.21684/2411-7978-2021-7-2-147-169.

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Modern challenges in a post-industrial society require further development of management systems for complex technical and technological phenomena and processes. Effective control of an object is possible if a controller, or a fuzzy controller, correctly generates the required control action. Recently, fuzzy controllers have been very popular. Fuzzy logical statements in this case help considering various nonlinear relationships. The synthesis of the fuzzy controller parameters allows for more efficient operation of the control system. A possible option for obtaining the best set of parameters

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41

Kroll, A., and S. Soldan. "On Data-driven Takagi-Sugeno Modeling of Heterogeneous Systems with Multidimensional Membership Functions." IFAC Proceedings Volumes 44, no. 1 (2011): 14994–99. http://dx.doi.org/10.3182/20110828-6-it-1002.00266.

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42

Al-Gallaf, E. A. "CLUSTERED BASED TAKAGI-SUGENO NEURO-FUZZY MODELING OF A MULTIVARIABLE NONLINEAR DYNAMIC SYSTEM." Asian Journal of Control 7, no. 2 (2008): 163–76. http://dx.doi.org/10.1111/j.1934-6093.2005.tb00385.x.

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43

Cheung, Ngaam J., Xue-Ming Ding, and Hong-Bin Shen. "OptiFel: A Convergent Heterogeneous Particle Swarm Optimization Algorithm for Takagi–Sugeno Fuzzy Modeling." IEEE Transactions on Fuzzy Systems 22, no. 4 (2014): 919–33. http://dx.doi.org/10.1109/tfuzz.2013.2278972.

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44

Vafamand, Navid, Mohammad Mehdi Arefi, and Alireza Khayatian. "Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter." ISA Transactions 74 (March 2018): 134–43. http://dx.doi.org/10.1016/j.isatra.2018.02.005.

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45

Barrios, José Ángel, Gerardo Maximiliano Méndez, and Alberto Cavazos. "Hybrid-Learning Type-2 Takagi–Sugeno–Kang Fuzzy Systems for Temperature Estimation in Hot-Rolling." Metals 10, no. 6 (2020): 758. http://dx.doi.org/10.3390/met10060758.

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Entry temperature estimation is a major concern for finishing mill set-up in hot strip mills. Variations in the incoming bar conditions, frequent product changes and measurement uncertainties may cause erroneous estimation, and hence, an incorrect mill set-up causing a faulty bar head-end. In earlier works, several varieties of neuro-fuzzy systems have been tested due to their adaptation capabilities. In order to test the combination of the simplicity offered by Takagi–Sugeno–Kang systems (also known as Sugeno systems) and the modeling power of type-2 fuzzy, in this work, hybrid-learning type-

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46

Wittich, Felix, and Andreas Kroll. "Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear regression with bounded errors." at - Automatisierungstechnik 69, no. 10 (2021): 836–47. http://dx.doi.org/10.1515/auto-2020-0157.

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Abstract In data-driven modeling besides the point estimate of the model parameters, an estimation of the parameter uncertainty is of great interest. For this, bounded error parameter estimation methods can be used. These are particularly interesting for problems where the stochastical properties of the random effects are unknown and cannot be determined. In this paper, different methods for obtaining a feasible parameter set are evaluated for the use with Takagi-Sugeno models. Case studies with simulated data and with measured data from a manufacturing process are presented.

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47

Yu, Yang, Wei Wang, and Shao Cheng Tong. "H^∞ Controller for Networked Nonlinear Manufacturing Process Interconnected Systems Based on T-S Fuzzy Model." Advanced Materials Research 299-300 (July 2011): 844–50. http://dx.doi.org/10.4028/www.scientific.net/amr.299-300.844.

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The problem of H∞ control for networked interconnected system with unknown disturbance is discussed. The Takagi-Sugeno (T-S) fuzzy model is adopted for fuzzy modeling of the networked interconnected system, considering the problems of time-delay and data packet dropouts for networked control. According to the Lyapunov stability theory and decentralized control theory of interconnected system, based on LMI method, the paper proposes the sufficient condition that ensures the asymptotic stability and satisfies the H∞ performance. Finally, the numerical example and simulations have demonstrated th

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48

Wang, Feng, and Xiao Ping Liu. "H_∞ Control of Flexible Joint Robot via T-S Fuzzy Model." Applied Mechanics and Materials 128-129 (October 2011): 894–97. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.894.

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In this paper, a H∞ control approach based on T-S fuzzy model for flexible joint robot is proposed. First, the Takagi and Sugeno (T-S) fuzzy model is applied to approximate the flexible joint robot. Then, a fuzzy controller is developed based on parallel distributed compensation principle (PDC), and H∞ performance is employed to restrain the influence of modeling error caused by variety of stiffness. The stability conditions for the flexible joint robot control system are proposed by Lyapunov function. Finally, the simulation results are given to show the effectiveness of the proposed method.

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49

Tang, Xin, Boyuan Li, and Haiping Du. "A Study on Dynamic Motion Planning for Autonomous Vehicles Based on Nonlinear Vehicle Model." Sensors 23, no. 1 (2022): 443. http://dx.doi.org/10.3390/s23010443.

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Autonomous driving technology, especially motion planning and the trajectory tracking method, is the foundation of an intelligent interconnected vehicle, which needs to be improved urgently. Currently, research on path planning methods has improved, but few of the current studies consider the vehicle’s nonlinear characteristics in the reference model, due to the heavy computational effort. At present, most of the algorithms are designed by a linear vehicle model in order to achieve the real-time performance at the cost of lost accuracy. To achieve a better performance, the dynamics and kinemat

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Wang, Wanru, Yonggang Wen, Liankun Sun, and Kaifan Ma. "H∞ Filter for Discrete-Time Takagi–Sugeno Fuzzy Systems with Time-Varying Delays via a Novel Wirtinger-Based Inequality." Discrete Dynamics in Nature and Society 2022 (June 29, 2022): 1–13. http://dx.doi.org/10.1155/2022/6317958.

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This paper is devoted to the analysis and design of H∞ filtering for discrete-time Takagi–Sugeno (TS) fuzzy systems with time-varying delays. By using the delay-partitioning method, more systematic information is introduced, which can reduce the conservatism of the conclusion. By constructing an appropriate Lyapunov functional and combining with a newly Wirtinger-based summation inequality, the existence condition of the H∞ filter is obtained. Thus, utilizing the contract matrix transformation method, the H∞ filter design for discrete-time TS fuzzy systems with time-varying delay based on LMI

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