scholarly journals Distributed Mirror Descent With Integral Feedback: Asymptotic Convergence Analysis of Continuous-Time Dynamics

2021 ◽  
Vol 5 (5) ◽  
pp. 1507-1512
Author(s):  
Youbang Sun ◽  
Shahin Shahrampour
Keyword(s):  
Convergence Analysis ◽  
Continuous Time ◽  
Asymptotic Convergence ◽  
Time Dynamics ◽  
Mirror Descent ◽  
Integral Feedback

scholarly journals Interval Observer Design for Metzlerian Takagi-Sugeno Systems

2020 ◽  
Author(s):  
Dušan Krokavec ◽  
Anna Filasová
Keyword(s):  
Continuous Time ◽  
Observer Design ◽  
Asymptotic Convergence ◽  
Observer Error ◽  
Analysis And Design ◽  
Interval Observer ◽  
Takagi Sugeno ◽  
Positive Observer ◽  
The Relationship

The generalized interval observer design conditions for continuous-time Metzlerian Takagi-Sugeno systems are presented in the paper. Attention is focused on the analysis and design guaranteeing the asymptotic convergence of the interval observer error and positivity of interval observer state. The relationship between the nonnegativity of the observer gains and the corresponding positive observer state attractiveness is also shown. The method presented extends and generalizes the results that recently appeared in the literature.

scholarly journals A Simple And Novel Algorithm For State Estimation Of Continuous Time Linear Stochastic Dynamic Systems Excited By Random Inputs

2019 ◽  
Vol 8 (10) ◽  
pp. 3559-3563
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Continuous Time ◽  
Stochastic Dynamic ◽  
Time Dynamic ◽  
Time Dynamics ◽  
Hardware Realization ◽  
Aerial Vehicle ◽  
Non Linear System ◽  
Stochastic Dynamic Systems

The major goal of this paper is to explore the effective state estimation algorithm for continuous time dynamic system under the lossy environment without increasing the complexity of hardware realization. Though the existing methods of state estimation of continuous time system provides effective estimation with data loss, the real time hardware realization is difficult due to the complexity and multiple processing. Kalman Filter and Particle Filer are fundamental algorithms for state estimation of any linear and non-linear system respectively, but both have its limitation. The approach adopted here, detect the expected state value and covariance, existed by random input at each stage and filtered the noisy measurement and replace it with predicted modified value for the effective state estimation. To demonstrate the performance of the results, the continuous time dynamics of position of the Aerial Vehicle is used with proposed algorithm under the lossy measurements scenario and compared with standard Kalman filter and smoothed filter. The results show that the proposed method can effectively estimate the position of Aerial Vehicle compared to standard Kalman and smoothed filter under the non-reliable sensor measurements with less hardware realization complexity.

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