2019
DOI: 10.1080/00207179.2019.1612097
|View full text |Cite
|
Sign up to set email alerts
|

Computing stabilising output-feedback gains for continuous-time linear time-varying systems through discrete-time periodic models

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2020
2020
2021
2021

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(2 citation statements)
references
References 30 publications
0
2
0
Order By: Relevance
“…In order to improve the accuracy of the system, some scholars have studied different control methods to control different systems. A methodology for the synthesis of time-varying static output-feedback gains, capable of stabilising continuous-time linear time-varying systems, was proposed in [22]. In [23], the state feedback nonlinear model predictive control law was approximated offline by the smooth function of the state.…”
Section: Introductionmentioning
confidence: 99%
“…In order to improve the accuracy of the system, some scholars have studied different control methods to control different systems. A methodology for the synthesis of time-varying static output-feedback gains, capable of stabilising continuous-time linear time-varying systems, was proposed in [22]. In [23], the state feedback nonlinear model predictive control law was approximated offline by the smooth function of the state.…”
Section: Introductionmentioning
confidence: 99%
“…In [24, 25], both the state and output stability of discrete systems based on a Lyapunov‐like function are discussed, and the control design method for synthesising and stabilising the system state is deduced. In [26], a feedback controller design method based on the discrete‐time periodic model is proposed for the FTS of the system state. In [27], new linear matrix inequality (LMI) conditions to the observer‐based control design problem for the periodic discrete‐time system are derived, which replaces the traditional Lyapunov‐based method.…”
Section: Introductionmentioning
confidence: 99%