Improvement of Takagi‐Sugeno fuzzy model for the estimation of nonlinear functions

Jiménez, Agustín; Al‐Hadithi, Basil Mohammed; Matı́a, Fernando; Haber‐Haber, Rodolfo

doi:10.1002/asjc.310

Cited by 19 publications

(13 citation statements)

References 41 publications

(62 reference statements)

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“…In the case when the matrix X is not of full rank, an effective approach with few computational effort, based on the well known parameters' weighting method [2], [1] and [7]. This method can also be used for parameters tuning of T-S model from local parameters obtained through the identification of a system in an operating region or from any physical input/output data.…”

Section: Estimation Of Fuzzy T-s Model's Parametersmentioning

confidence: 99%

“…In this case, the factor γ represents the degree of confidence of the parameters initially estimated [2], [3], [4] and [7].…”

Section: Estimation Of Fuzzy T-s Model's Parametersmentioning

confidence: 99%

“…In [1] and [7] new and efficient approaches are presented to improve the local and global estimation of T-S model. The first approach uses the minimum norm method to search for an exact optimum solution at the expense of increasing complexity and computational cost.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Control Using Feedback Linearization for a Generalized T-S Model

Jiménez

Al‐Hadithi

Pérez-Oria

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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In this paper, a fuzzy feedback linearization is used to control nonlinear systems described by Takagi-Suengo (T-S) fuzzy systems. In this work, an optimal controller is designed using the linear quadratic regulator (LQR). The well known weighting parameters approach is applied to optimize local and global approximation and modelling capability of T-S fuzzy model to improve the choice of the performance index and minimize it. The approach used here can be considered as a generalized version of T-S method. Simulation results indicate the potential, simplicity and generality of the estimation method and the robustness of the proposed optimal LQR algorithm.

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Section: Estimation Of Fuzzy T-s Model's Parametersmentioning

confidence: 99%

“…In this case, the factor γ represents the degree of confidence of the parameters initially estimated [2], [3], [4] and [7].…”

Section: Estimation Of Fuzzy T-s Model's Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Control Using Feedback Linearization for a Generalized T-S Model

Jiménez

Al‐Hadithi

Pérez-Oria

et al. 2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Self Cite

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show abstract

“…The cost effective design and implementation of fuzzy controllers is challenging in this regard. Several fuzzy control approaches and fuzzy models are successful in various applications.…”

Section: Introductionmentioning

confidence: 99%

Cascade Control System‐Based Cost Effective Combination of Tensor Product Model Transformation and Fuzzy Control

Precup¹,

Petriu²,

Rădac³

et al. 2014

Asian Journal of Control

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This paper proposes the combination of tensor product (TP)-based model transformation and of fuzzy control as a cost effective cascade control system (CS) structure. A novel two-step design approach is suggested. A parallel distributed compensation technique is used in the first step in the TP-based design of the inner state feedback control loops which is next characterized by simple models using equivalent state feedback matrices. The modulus optimum method and the modal equivalent principle are used in the second step to design the Mamdani PI-fuzzy controller in the outer control loops. The cost effective feature of the original cascade CS combination concerns the simplicity of the CS structure, of the controllers and of the design approach. The presentation is focused on the level control in horizontal three tank systems where two cascade CS structures are separately designed to control two water levels. The efficiency of the cascade CS structure is proved by real-time experimental results for a laboratory three tank system. Comparisons with the Mamdani PI-fuzzy CS and with the cascade combination of TP and PI CS are included. 38 8 8 8 8 8 8 3 8 91 Fig. 7. Real-time response of cascade combination of TP and fuzzy control system structure with respect to reference and disturbance inputs: level h3 versus time.

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“…T‐S fuzzy systems proposed by Takagi and Sugeno in 1985 are an effective tool for approximation of uncertain nonlinear systems . Due to crisp mathematical functions of fuzzy rule consequences, T‐S fuzzy systems provide a reasonable framework for modeling by decomposition of a nonlinear system into a collection of local linear models . Based on data‐driven learning, T‐S fuzzy systems identification and its successful application in various fields have gained considerable interest and effort in the past decades .…”

Section: Introductionmentioning

confidence: 99%

Dynamic T‐S Fuzzy Systems Identification Based on Sparse Regularization

Luo¹,

Sun²,

Liu³

2014

Asian Journal of Control

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Fuzzy system identification suffers from rules explosion, i.e., a large number of fuzzy rules are required for fuzzy systems with high dimension input variable. In this paper, a dynamic algorithm is proposed to address T-S fuzzy system identification with both sparsity and dynamic clustering, named as dynamic sparse fuzzy inference systems (D-sparseFIS). Due to two different estimation approaches of fuzzy rule consequence parameter, i.e., global estimation and local estimation, D-sparseFIS.local and D-sparseFIS.global methods are exploited with local least squares and global least squares estimation based on sparse regularization. Both two dynamic algorithms can guarantee a minimal number of fuzzy rules and nonzero consequence parameters are equipped in T-S fuzzy system. Finally, some numerical experiments are presented to illustrate the effectiveness of the proposed algorithms. . Her research interests include system identification, sparse optimization methods and fuzzy discrete event systems.Fuchun Sun (S'94-M'98-SM'07) received the Ph.D degree from the

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Improvement of Takagi‐Sugeno fuzzy model for the estimation of nonlinear functions

Cited by 19 publications

References 41 publications

Optimal Control Using Feedback Linearization for a Generalized T-S Model

Optimal Control Using Feedback Linearization for a Generalized T-S Model

Cascade Control System‐Based Cost Effective Combination of Tensor Product Model Transformation and Fuzzy Control

Dynamic T‐S Fuzzy Systems Identification Based on Sparse Regularization

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