Static Output Feedback Control for T-S Fuzzy Systems via a Successive Convex Optimization Algorithm

This article focuses on developing a static output feedback (SOF) control scheme for Takagi-Sugeno fuzzy systems. The proposed SOF controller does not share the same premise membership functions with the model, which permits enhancing the flexibility in controller design and implementation. By contr...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on fuzzy systems Ročník 30; číslo 10; s. 4298 - 4309
Hlavní autoři: Ren, Yingying, Ding, Da-Wei, Li, Qing, Xie, Xiangpeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Approximation algorithms
Bilinear matrix inequality (BMI)
Closed loop systems
Computational geometry
Control systems design
Controllers
Convex functions
Convexity
Feedback control
Fuzzy control
Fuzzy systems
Heuristic algorithms
Heuristic methods
Initial conditions
Iterative methods
Optimization
Output feedback
State feedback
static output feedback (SOF)
successive convex optimization (SCO) algorithm
Takagi–Sugeno (T–S) fuzzy systems
ISSN:1063-6706, 1941-0034
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This article focuses on developing a static output feedback (SOF) control scheme for Takagi-Sugeno fuzzy systems. The proposed SOF controller does not share the same premise membership functions with the model, which permits enhancing the flexibility in controller design and implementation. By contrast with the state feedback case, the SOF control generally results in nonconvex design conditions. To circumvent this problem, we develop a successive convex optimization algorithm, which is based on solving a sequence of more tractable convex optimization problems obtained by approximating the nonconvex constraints with some convex ones. As a heuristic algorithm, the validity of the developed successive convex optimization algorithm is highly affected by initial conditions, and, recognizing this, we put forward an iterative procedure for determining the feasible initial condition. Finally, two illustrative examples are presented to validate the efficiency of the proposed algorithms.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2022.3146987