Introduction to State Estimation of High-Rate System Dynamics

Hong, Jonathan; Laflamme, Simon; Dodson, Jacob; Joyce, Bryan

doi:10.3390/s18010217

Cited by 38 publications

(26 citation statements)

References 82 publications

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“…Большое число теоретических результатов в области адаптивного управления создает возможности разнообразных, актуальных и интересных практических применений, в их числе: навигация и управление морских подвижных объектов (Amerongen, 2005), включая предотвращение столкновений; перехват быстро-ПРОФЕССОРСКИЙ ЖУРНАЛ «Технические науки» Семушин И.В. маневрирующих целей (Josef, 2009); оценивание состояния скоротечных событий (Hong et. al., 2018); обработка медицинских данных (Semushin et al, 2016); оценивание характеристик каналов связи для OFDM-или DMT-технологий (Semushin et al, 2017); контроль процессов в электроэнергетике (Zanni et al, 2017) и многое другое (Auger et.…”

Section: практические применения и признательностиunclassified

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2019

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Section: практические применения и признательностиunclassified

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2019

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“…High-rate dynamic systems are defined as engineering systems experiencing high-amplitude disruptions (acceleration >100 g n ) within a very short duration (<100 ms) [1]. Examples of high-rate systems include blast mitigation mechanisms, hypersonic aircraft, and advanced weaponry.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Time-Frequency Methods for Real-Time Application to High-Rate Dynamic Systems

et al. 2020

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High-rate dynamic systems are defined as engineering systems experiencing dynamic events of typical amplitudes higher than 100 gn for a duration of less than 100 ms. The implementation of feedback decision mechanisms in high-rate systems could improve their operations and safety, and even be critical to their deployment. However, these systems are characterized by large uncertainties, high non-stationarities, and unmodeled dynamics, and it follows that the design of real-time state-estimators for such purpose is difficult. In this paper, we compare the promise of five time-frequency representation (TFR) methods at conducting real-time state estimation for high-rate systems, with the objective of providing a path to designing implementable algorithms. In particular, we examine the performance of the short-time Fourier transform (STFT), wavelet transformation (WT), Wigner–Ville distribution (WVD), synchrosqueezed transform (SST), and multi-synchrosqueezed transform (MSST) methods. This study is conducted using experimental data from the DROPBEAR (Dynamic Reproduction of Projectiles in Ballistic Environments for Advanced Research) testbed, consisting of a rapidly moving cart on a cantilever beam that acts as a moving boundary condition. The capability of each method at extracting the beam’s fundamental frequency is evaluated in terms of precision, spectral energy concentration, computation speed, and convergence speed. It is found that both the STFT and WT methods are promising methods due to their fast computation speed, with the WT showing particular promise due to its faster convergence, but at the cost of lower precision on the estimation depending on circumstances.

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“…Insufficient convergence speeds of observers can negatively influence the observation process in real-time applications, including vehicle dynamics and terrain mobility control. At that time, in order to estimate a fast dynamics of tire-terrain interrelation, the observer should have the convergence rate in several times faster than considered plant has [4]. In an article by Bogdanski and Best [5], the UKF, EKF and PF are used for the identification of tire and vehicle parameters; results obtained in the article testify that the PF's convergence is slow, hence, this observer is recommended for simple models and systems.…”

Section: Introductionmentioning

confidence: 99%

An estimation accuracy of state observers under uncertain initial conditions

Lozynskyy¹,

Demkiv²,

Vantsevich³

et al. 2019

Math. Model. Comput.

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A fast convergence speed of an observer helps improve the capability to track the states of a system for an arbitrary divergence between a real and an estimated initial conditions. This property of the observers is significantly useful if a system has fast dynamics and its states change rapidly. Thus, the convergence time is one of the main performance criteria of linear and non-linear state observers. This article presents a comparative analysis of observers for both linear and nonlinear systems in terms of the time of convergence of the observers. The following observers was chosen for this study: the Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), Luenberger observer (LO), and fuzzy-based Luenberger observer (Fuzzy-LO). The listed observers were studied using a non-linear mathematical model of an open-link locomotion module, which movements were studied in stochastic terrain conditions. The mathematical model was then simplified and simulated as a linear model with the purpose to estimate the efficiency of the linear observers. The Fuzzy-LO with an adaptive gain to the estimation error gives better results than the LO, especially in steady states. The PF with a simple Gaussian distribution provides a lower convergence speed than the KF, EKF, and UKF. To faster the convergence of the PF, a novel approach, PF*, that utilizes mixture probability density function of the distribution of initial particles was introduced in the article.

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Introduction to State Estimation of High-Rate System Dynamics

Cited by 38 publications

References 82 publications

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A Comparison of Time-Frequency Methods for Real-Time Application to High-Rate Dynamic Systems

An estimation accuracy of state observers under uncertain initial conditions

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