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Open AccessArticle
Probabilistic Estimation and Control of Dynamical Systems Using Particle Filter with Adaptive Backward Sampling
by
Taketo Omi
1
and
Toshiaki Omori
Toshiaki Omori 1,2,*
1
Department of Electrical and Electronic Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
2
Center for Mathematical and Data Sciences, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
*
Author to whom correspondence should be addressed.
Entropy 2024, 26(8), 653; https://doi.org/10.3390/e26080653 (registering DOI)
Submission received: 26 May 2024
/
Revised: 5 July 2024
/
Accepted: 17 July 2024
/
Published: 30 July 2024
Abstract
Estimating and controlling dynamical systems from observable time-series data are essential for understanding and manipulating nonlinear dynamics. This paper proposes a probabilistic framework for simultaneously estimating and controlling nonlinear dynamics under noisy observation conditions. Our proposed method utilizes the particle filter not only as a state estimator and a prior estimator for the dynamics but also as a controller. This approach allows us to handle the nonlinearity of the dynamics and uncertainty of the latent state. We apply two distinct dynamics to verify the effectiveness of our proposed framework: a chaotic system defined by the Lorenz equation and a nonlinear neuronal system defined by the Morris–Lecar neuron model. The results indicate that our proposed framework can simultaneously estimate and control complex nonlinear dynamical systems.
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MDPI and ACS Style
Omi, T.; Omori, T.
Probabilistic Estimation and Control of Dynamical Systems Using Particle Filter with Adaptive Backward Sampling. Entropy 2024, 26, 653.
https://doi.org/10.3390/e26080653
AMA Style
Omi T, Omori T.
Probabilistic Estimation and Control of Dynamical Systems Using Particle Filter with Adaptive Backward Sampling. Entropy. 2024; 26(8):653.
https://doi.org/10.3390/e26080653
Chicago/Turabian Style
Omi, Taketo, and Toshiaki Omori.
2024. "Probabilistic Estimation and Control of Dynamical Systems Using Particle Filter with Adaptive Backward Sampling" Entropy 26, no. 8: 653.
https://doi.org/10.3390/e26080653
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