Stability Analysis and Controller Design of Discrete Interval Type-2 Fuzzy Systems

2013 ◽  
Vol 16 (4) ◽  
pp. 1091-1104 ◽  
Author(s):  
Long Sheng ◽  
Xiaoyu Ma
Keyword(s):  
Stability Analysis ◽  
Fuzzy Systems ◽  
Controller Design ◽  
Discrete Interval ◽  
Interval Type

Observer-based H∞ controller design for interval type-2 T–S fuzzy systems

2016 ◽  
Vol 177 ◽  
pp. 9-25 ◽  
Author(s):  
Tao Zhao ◽  
Zhenbo Wei ◽  
Songyi Dian ◽  
Jian Xiao
Keyword(s):  
Fuzzy Systems ◽  
Controller Design ◽  
Interval Type

scholarly journals Event-Triggered Mixed Non-Fragile and Measurement Quantization Filtering Design for Interval Type-2 Fuzzy Systems

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 1533-1545
Author(s):  
Chunsong Han ◽  
Dingding Song ◽  
Guangtao Ran ◽  
Jiafeng Yu
Keyword(s):  
Fuzzy Systems ◽  
Interval Type ◽  
Event Triggered

scholarly journals Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Wen-Jer Chang ◽  
Yu-Wei Lin ◽  
Yann-Horng Lin ◽  
Chin-Lin Pen ◽  
Ming-Hsuan Tsai
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Actuator Saturation ◽  
Design Method ◽  
Fuzzy Model ◽  
The Stability ◽  
Interval Type ◽  
Takagi Sugeno

In many practical systems, stochastic behaviors usually occur and need to be considered in the controller design. To ensure the system performance under the effect of stochastic behaviors, the controller may become bigger even beyond the capacity of practical applications. Therefore, the actuator saturation problem also must be considered in the controller design. The type-2 Takagi-Sugeno (T-S) fuzzy model can describe the parameter uncertainties more completely than the type-1 T-S fuzzy model for a class of nonlinear systems. A fuzzy controller design method is proposed in this paper based on the Interval Type-2 (IT2) T-S fuzzy model for stochastic nonlinear systems subject to actuator saturation. The stability analysis and some corresponding sufficient conditions for the IT2 T-S fuzzy model are developed using Lyapunov theory. Via transferring the stability and control problem into Linear Matrix Inequality (LMI) problem, the proposed fuzzy control problem can be solved by the convex optimization algorithm. Finally, a nonlinear ship steering system is considered in the simulations to verify the feasibility and efficiency of the proposed fuzzy controller design method.

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