Discrete-Time $$\mathcal {H}_{\infty }$$ Integral Control Via LMIs Applied to a Furuta Pendulum

2022 ◽  
Author(s):  
Uiliam Nelson Lendzion Tomaz Alves ◽  
Ricardo Breganon ◽  
Luiz Eduardo Pivovar ◽  
João Paulo Lima Silva de Almeida ◽  
Gustavo Vendrame Barbara ◽  
...  
Keyword(s):  
Discrete Time ◽  
Integral Control

scholarly journals The discrete-time tracking problem with H∞ model matching approach plus integral control

2019 ◽  
Vol 292 ◽  
pp. 01018
Author(s):  
Murat Akın ◽  
Tankut Acarman
Keyword(s):  
Discrete Time ◽  
Closed Loop ◽  
Controller Design ◽  
Model Matching ◽  
Matching Problem ◽  
Design Algorithm ◽  
Integral Control ◽  
Loop Transfer Function ◽  
Model Transfer ◽  
Static Output

In this study, the discrete-time H∞ model matching problem with integral control by using 2 DOF static output feedback is presented. First, the motivation and the problem is stated. After presenting the notation, the two lemmas toward the discrete-time H∞ model matching problem with integral control are proven. The controller synthesis theorem and the controller design algorithm is elaborated in order to minimize the H∞ norm of the closed-loop transfer function and to maximize the closed-loop performance by introducing the model transfer matrix. In following, the discrete-time H∞ MMP via LMI approach is derived as the main result. The controller construction procedure is implemented by using a well-known toolbox to improve the usability of the presented results. Finally, some conclusions are given.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

Discrete-Time Delay Dynamics of Boundedly Rational Monopoly

2012 ◽  
Author(s):  
Akio Matsumato ◽  
Ferenc Szidarovsky
Keyword(s):  
Time Delay ◽  
Discrete Time ◽  
Discrete Time Delay
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