| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 496175 | Applied Soft Computing | 2012 | 11 Pages |
An efficient approach is presented to improve the local and global approximation and modelling capability of Takagi–Sugeno (T–S) fuzzy model. The main aim is obtaining high function approximation accuracy. The main problem is that T–S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the use of the T–S method because this type of membership function has been widely used during the last two decades in the stability, controller design and are popular in industrial control applications. The approach developed here can be considered as a generalized version of T–S method with optimized performance in approximating nonlinear functions. A simple approach with few computational effort, based on the well known parameters’ weighting method is suggested for tuning T–S parameters to improve the choice of the performance index and minimize it. A global fuzzy controller (FC) based Linear Quadratic Regulator (LQR) is proposed in order to show the effectiveness of the estimation method developed here in control applications. Illustrative examples of an inverted pendulum and Van der Pol system are chosen to evaluate the robustness and remarkable performance of the proposed method and the high accuracy obtained in approximating nonlinear and unstable systems locally and globally in comparison with the original T–S model. Simulation results indicate the potential, simplicity and generality of the algorithm.
Graphical abstractAn efficient approach is presented to improve the local and global modelling of Takagi–Sugeno (T–S) fuzzy model. The main problem is that T–S method can not be applied when the membership functions are overlapped by pairs. A simple approach with few computational efforts, based on the well known parameters’ weighting method is suggested for tuning T–S parameters to improve the choice of the performance index and minimize it. A global fuzzy controller based Linear Quadratic Regulator is proposed to show the effectiveness of the estimation method developed here in control applications.Figure optionsDownload full-size imageDownload as PowerPoint slideHighlights► An efficient approach is presented to improve the local and global approximation of Takagi–Sugeno (T–S) fuzzy model. ► The approach developed here can be considered as a generalized version of T–S method. ► The well known parameters’ weighting method is suggested for tuning T–S parameters. ► A global fuzzy controller (FC) based Linear Quadratic Regulator (LQR) is proposed in order to show the effectiveness of the estimation method.
