Relaxed static output feedback control for discrete-time Takagi-Sugeno fuzzy systems: A switching sequence convex optimization algorithm

In this paper, a new switching sequence convex optimization (SSCO) algorithm is proposed for solving non-convex optimization problem with complex time-varying relaxation matrix structures that arises during output feedback design. Firstly, the introduced time-varying relaxation matrix combines the m...

Full description

Saved in:
Bibliographic Details
Published in:Information sciences Vol. 678; p. 120966
Main Authors: Gao, Jingjing, Xie, Xiangpeng, Xia, Jianwei
Format: Journal Article
Language:English
Published: Elsevier Inc 01.09.2024
Subjects:
Optimization algorithm
Static output feedback
Takagi-Sugeno fuzzy systems
Time-varying relaxation matrix technique
Static output feedback
Optimization algorithm
Takagi-Sugeno fuzzy systems
Time-varying relaxation matrix technique
ISSN:0020-0255, 1872-6291
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, a new switching sequence convex optimization (SSCO) algorithm is proposed for solving non-convex optimization problem with complex time-varying relaxation matrix structures that arises during output feedback design. Firstly, the introduced time-varying relaxation matrix combines the membership functions and the designed switching mechanism to adjust the positive and negative terms of the inequality constraints. As a result, relaxed controller design conditions with complex matrix structures are established. The proposed SSCO algorithm employs switching optimization variables and inner approximation strategy, which is able to compute non-convex optimization problems with complex matrix structures more flexibly and converge quickly. It is worth noting that the implementation of the SSCO algorithm requires a set of strictly feasible initial solutions. Therefore, an initialization iterative algorithm is proposed, which overcomes the difficulties of transforming the solving problem into a typical non-convex optimization problem and linearizing multiple different concave parts, by which a set of optimized feasible solutions are obtained. Finally, simulation examples are used to demonstrate the superiority of the design scheme proposed in this paper.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2024.120966