GDL

Journal from gdlhub / 2021-08-24 15:04:19
Oleh : Majid Khak Pour, Sehraneh Ghaemi & Mohammad Ali Badamchizadeh, Fuzzy Information and Engineering
Dibuat : 2021-08-24, dengan 0 file

Keyword : Compensation control, Fuzzy Logicquadratic Lyapunovstability, Takagi-Sugeno-Kang (TSK)Interval type-2 fuzzy systems
Url : http://www.tandfonline.com/doi/full/10.1080/16168658.2019.1706957
Sumber pengambilan dokumen : Web

This paper proposes a new method for designing a state feedback controller for stabilizing and also making the desired performance of Interval Type-2 Takagi–Sugeno-Kang Fuzzy Logic Systems (fIT2 TSK FLS). By using the advantages of WU-Mendel Uncertainty Bounds (WM-UB) method, this paper proposes a new approach for Single-Input and Single-Output (SISO) and Multi-Input and Multi-Output (MIMO) interval type-2 TSK systems, which is based on the Hybrid Compensation Control (HCC) approach. The advantages of this method are no necessity to solving any Linear Matrix Inequalities (LMIs) to find a quadratic Lyapunov function for designing the stabilizer controller and also, the designed controller could compensate the time-varying variations. It should be noted that the inference engine is formulated in closed form and does not require using any type of reduction. Some examples have been conducted in our study to demonstrate the effectiveness of the proposed control design approach and compare this method with the previous approaches. All results illustrate good control performance.

Deskripsi Alternatif :

This paper proposes a new method for designing a state feedback controller for stabilizing and also making the desired performance of Interval Type-2 Takagi–Sugeno-Kang Fuzzy Logic Systems (fIT2 TSK FLS). By using the advantages of WU-Mendel Uncertainty Bounds (WM-UB) method, this paper proposes a new approach for Single-Input and Single-Output (SISO) and Multi-Input and Multi-Output (MIMO) interval type-2 TSK systems, which is based on the Hybrid Compensation Control (HCC) approach. The advantages of this method are no necessity to solving any Linear Matrix Inequalities (LMIs) to find a quadratic Lyapunov function for designing the stabilizer controller and also, the designed controller could compensate the time-varying variations. It should be noted that the inference engine is formulated in closed form and does not require using any type of reduction. Some examples have been conducted in our study to demonstrate the effectiveness of the proposed control design approach and compare this method with the previous approaches. All results illustrate good control performance.

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