Sampled-data State-feedback Control under Disturbances and Measurement Noises⋆

2020 ◽  
Author(s):  
Igor Furtat ◽  
Alexey Peregudin
Keyword(s):  
Feedback Control ◽  
State Feedback ◽  
State Feedback Control ◽  
Sampled Data ◽  
Measurement Noises

Optimal Sampled–Data State Feedback Control of Linear Systems

2014 ◽  
Vol 47 (3) ◽  
pp. 5556-5561 ◽  
Author(s):  
Matheus Souza ◽  
Gabriela W.G. Vital ◽  
José C. Geromel
Keyword(s):  
Feedback Control ◽  
Linear Systems ◽  
State Feedback ◽  
State Feedback Control ◽  
Sampled Data ◽  
Control Of Linear Systems

Sampled-Data State Feedback Control for the Set Stabilization of Boolean Control Networks

2020 ◽  
Vol 50 (4) ◽  
pp. 1580-1589 ◽  
Author(s):  
Shiyong Zhu ◽  
Yang Liu ◽  
Jungang Lou ◽  
Jianquan Lu ◽  
Fuad E. Alsaadi
Keyword(s):  
Feedback Control ◽  
State Feedback ◽  
State Feedback Control ◽  
Sampled Data ◽  
Control Networks ◽  
Set Stabilization

scholarly journals State-feedback control of LPV sampled-data systems

2000 ◽  
Vol 6 (2-3) ◽  
pp. 145-170 ◽  
Author(s):  
K. Tan ◽  
K. M. Grigoriadis
Keyword(s):  
Feedback Control ◽  
State Feedback ◽  
The State ◽  
State Feedback Control ◽  
Control Synthesis ◽  
Linear Matrix ◽  
Compact Set ◽  
Data Systems ◽  
Sampled Data ◽  
Space Data

In this paper, we address the analysis and the state-feedback synthesis problems for linear parameter-varying (LPV) sampled-data control systems. We assume that the state-space data of the plant and the sampling interval depend on parameters that are measurable in real-time and vary in a compact set with bounded variation rates. We explore criteria such as the stability, the energy-to-energy gain (inducedL2norm) and the energy-to-peak gain (inducedL2-to-L∞norm) of such sampled-data LPV systems using parameter-dependent Lyapunov functions. Based on these analysis results, the sampled-data state-feedback control synthesis problems are examined. Both analysis and synthesis conditions are formulated in terms of linear matrix inequalities that can be solved via efficient interior-point algorithms.

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