Content area

Abstract

This paper is devoted to the topic of robust stabilization for uncertain multi-machine power systems (MMPSs) using input delay-based sampled-data control. The study explores the sampled-data control for a nonlinear MMPS with parametric uncertainties exacerbated with sector saturating actuators. A saturated controller is considered for the system to recover the loss of stability in the continuous time domain. An approach, comprising linear matrix inequality technique and average dwell time method, is exploited, employing proper Lyapunov–Krasovskii functional, to show that the proposed saturated sampled-data control renders exponential stability. More precisely, the existence condition of sampled-data control law is developed in form of linear matrix inequalities. In order to simplify the derivation in main results, Schur complement and Wirtinger inequalities are used. Through the simulation tests on a two-machine infinite bus system model, the effectiveness and robustness of the proposed controller over the time delays and parameter uncertainties are verified.

Details

Title
LMI approach-based sampled-data control for uncertain systems with actuator saturation: application to multi-machine power system
Author
Santra Srimanta 1   VIAFID ORCID Logo  ; Maya, Joby 2   VIAFID ORCID Logo  ; Sathishkumar, M 3   VIAFID ORCID Logo  ; Marshal, Anthoni S 4   VIAFID ORCID Logo 

 Technion-Israel Institute of Technology, Faculty of Mechanical Engineering, Haifa, Israel (GRID:grid.6451.6) (ISNI:0000000121102151) 
 KCG College of Technology, Department of Mathematics, Chennai, India (GRID:grid.252262.3) (ISNI:0000 0001 0613 6919) 
 National Cheng Kung University, Department of Mechanical Engineering, Tainan, Taiwan (GRID:grid.64523.36) (ISNI:0000 0004 0532 3255) 
 Anna University Regional Office, Department of Mathematics, Coimbatore, India (GRID:grid.252262.3) (ISNI:0000 0001 0613 6919) 
Pages
967-982
Publication year
2022
Publication date
Jan 2022
Publisher
Springer Nature B.V.
ISSN
0924090X
e-ISSN
1573269X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2616480359
Copyright
© The Author(s), under exclusive licence to Springer Nature B.V. 2021.