Robust Adaptive Generalized Predictive Control Based on Takagi-Sugeno Fuzzy Model

2021 ◽  
Vol 14 (3) ◽  
pp. 135
Author(s):  
Mohamed Lamine Fas ◽  
Mohamed Benrabah ◽  
Abderrezak Guessoum
Author(s):  
Bin Wang ◽  
Jianwei Zhang ◽  
Delan Zhu ◽  
Diyi Chen

This paper investigates the fuzzy predictive control for a class of nonlinear system with constrains under the condition of noise. Based on the fuzzy linearization theory, a class of nonlinear systems can be described by the Takagi–Sugeno (T–S) fuzzy model. The T–S fuzzy model and predictive control are combined to stabilize the proposed class of nonlinear system, and the detailed mathematical derivation is given. Moreover, the designed controller has been optimized even if the system is constrained by output and control input, or perturbed by external disturbances. Finally, numerical simulations including three-dimensional Lorenz system, four-dimensional Chen system and five-dimensional nonlinear system with external disturbances are presented to demonstrate the universality and effectiveness of the proposed scheme. The approach proposed in this paper is simple and easy to implement and also provides reference for relevant nonlinear systems.


Automatika ◽  
2021 ◽  
Vol 63 (1) ◽  
pp. 49-63
Author(s):  
Mohammad Sarbaz ◽  
Iman Zamani ◽  
Mohammad Manthouri ◽  
Asier Ibeas

2018 ◽  
Vol 9 (2) ◽  
pp. 132-137
Author(s):  
Yuan Li ◽  
Jinwen Zheng ◽  
Zhaoguo Jiang ◽  
Qinglin Wang

Author(s):  
Nguyen Tuan Hung ◽  
Idris Ismail ◽  
Nordin Saad ◽  
Lemma Tufa ◽  
Rosdiazli Ibrahim

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