Convergence of Particle Filter for Output Feedback Control

Polisetty, Venkata Goutham and Jampana, Phanindra Varma (2020) Convergence of Particle Filter for Output Feedback Control. In: 18th European Control Conference 2020, ECC 2020, 12-15 May 2020.

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In the existing literature, convergence results for particle filters are given explicitly only for the case when the underlying dynamic model is a Markov process. When output feedback control is used, the evolution of the state process is no longer Markovian due to the dependence of inputs on the outputs. In this paper, it is shown that the random probability measures produced by the particle filter converge to the true prior and posterior measures in this nonMarkovian case. Firstly, it is proved that the recursive equations relating the prior and posterior measures continue to hold for output feedback control. These recursive equations are then used to show the required convergence of the random measures. Finally, the convergence is also illustrated using simulations on a nonlinear dynamical system.

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IITH Creators:
IITH CreatorsORCiD
Jampana, Phanindra Varma
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Convergence results; Output feedback controls; Particle filter; Probability measures; Random measures; Recursive equations; State process; Underlying dynamics;
Subjects: Chemical Engineering
Chemical Engineering > Metallurgy
Chemical Engineering > Biochemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 26 Jun 2021 08:53
Last Modified: 26 Jun 2021 08:53
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