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Information Extraction Using Spectral Analysis of the Chattering of the smooth variable structure filter

IEEE ACCESS 2023年 11卷 104992-105008页

作者： Saeedzadeh, Ahsan Setoodeh, Peyman Alavi, Marjan Habibi, Saeid McMaster Univ Dept Mech Engn Hamilton ON L8S 4L8 Canada W Booth Sch Engn Practice & Technol Hamilton ON L8S 4L8 Canada

smooth variable structure filter (SVSF) is a model-based robust nonlinear filtering technique, based on the variable structure concept formulated in a predictor-corrector form. It is used for estimating the states of a system and is robust against noise and modeling uncertainties. It ensures stability in the face of model mismatch resulting from a poor model or fault, at the expense of corrective actions, which cause chattering. The chattering contains mismatch footprints that can be exploited to identify system faults and determine their severity. In this paper, information extraction from chattering is investigated to identify model mismatch based on the spectral contents of the chattering signal. To verify the effectiveness of the developed framework for chattering analysis, two case studies are considered. First, the power spectrum of the chattering signal has been employed to identify mismatch and the potential of recovering the temporal information of the model mismatch from the spectrogram is studied, using Short Time Fourier Transform (STFT) for an underdamped second-order system. Then, the proposed strategy is applied to detect and measure the severity of leakage and friction faults as well as the bulk modulus mismatch in an electro-hydraulic actuator.

关键词： Fault detection information extraction smooth variable structure filter spectral analysis STFT

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Reliable state of charge and state of health estimation using the smooth variable structure filter

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CONTROL ENGINEERING PRACTICE 2018年 77卷 1-14页

作者： Afshari, Hamed Hossein Attari, Mina Ahmed, Ryan Delbari, Ali Habibi, Saeid Shoa, Tina McMaster Univ Ctr Mechatron & Hybrid Technol Hamilton ON Canada Cadex Elect Inc Vancouver BC Canada

This paper introduces a reliable strategy for the state of charge (SOC) and the state of health (SOH) estimation of healthy and aged Lithium polymer cells. Dynamics of the cell are modeled using some equivalent circuit models and parameters of each model are calculated by adaptive particle swarm optimization. The modeling process involves modeling and parametric uncertainties as well as measurement and instrumentation noise. They may degrade the performance of an optimal filter for SOC and SOH estimation. To alleviate effects of such uncertain factors, the smooth variable structure filter (SVSF) is implemented. The SVSF is a novel robust state estimation method that benefits from the robustness property of variable structure systems. The performance of the SVSF is compared with the extended Kalman filter (EKF) for real-time SOC estimation of a healthy and an aged Lithium polymer cell. The paper moreover presents a novel method for SOH estimation using the SVSF's chattering signal and without the need for modeling the cell undergoes aging. Experiments show performance benefits of the SVSF for reliable SOC and SOH estimation of healthy and aged Lithium polymer cells.

关键词： State of charge estimation smooth variable structure filter Modeling uncertainty Experimental study

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State of Temperature Estimation of Li-Ion Batteries Using 3rd Order smooth variable structure filter

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IEEE ACCESS 2023年 11卷 119078-119089页

作者： Ebrahimi, Farzaneh Ahmed, Ryan Habibi, Saeid McMaster Univ Ctr Mechatron & Hybrid Technol CMHT Dept Mech Engn Hamilton ON L8S 4L8 Canada

The Battery Management System plays a critical role in ensuring the longevity, safety, and optimal performance of batteries by performing state of charge and health estimation, thermal management, cell balancing, and charge control. Thermal management is a crucial component that is responsible for temperature monitoring and control, managing heat generation and dissipation, preventing thermal runaway, and optimizing battery performance. This paper includes several original contributions. (1) A four-state lumped thermal model is introduced to model the core and surface temperatures of the battery. (2) Accordingly, various characterization tests were conducted on a lithium-ion Prismatic battery to log the thermal behavior of the battery. The third-order Equivalent Circuit Model is used to calculate the generated heat inside the cell using the measured physical parameters such as voltage, and current. (3) Machine learning methods like Particle Swarm Optimization and Genetic Algorithm are used and compared to determine the parameters of the thermal model. (4) A novel, reliable 3rd order smooth variable structure filter is suggested in this work and evaluated against the Extended Kalman filter, SVSF, and 2nd-order SVSF. The proposed strategy demonstrated higher accuracy compared to the abovementioned filters.

关键词： Electric vehicles Li-Ion batteries smooth variable structure filter state estimation state of temperature (SOT) thermal management

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Lattice smooth variable structure filter for Maneuvering Target Tracking with Model Uncertainty

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2023年第4期47卷 1689-1701页

作者： Jiao, Yuzhao Lou, Taishan Zhao, Liangyu Zhao, Hongmei Lu, Yingbo Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China

This paper proposes a new lattice smooth variable structure filter (LSVSF) for maneuvering target tracking with model uncertainty. Under the assumption that the probability density function (PDF) is Gaussian-distributed, the nonlinear smooth variable structure filter (SVSF) framework is reconstructed by Bayesian theory. The optimal smooth boundary layer (OSBL) calculation form of the SVSF in a nonlinear framework is proposed. Then, based on lattice sampling methods with low computational complexity, the LSVSF algorithm is obtained. Finally, the LSVSF algorithm is verified on the maneuvering target tracking problem with model uncertainty by three scenarios: uniform motion (UM), coordinated turn (CT) motion and mixed motion (UM and CT). According to the simulation, the proposed LSVSF algorithm has superior tracking accuracy and robustness.

关键词： Lattice sampling smooth variable structure filter Optimal smooth boundary layer Nonlinear uncertainty systems Maneuvering target tracking

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An Improved smooth variable structure filter and Its Application in Ship Wave filtering

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2021年第3期45卷 711-719页

作者： Jiao, Yuzhao Zhao, Hongmei Wang, Xiaolei Lou, Taishan Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou Peoples R China

During the movement of the ship, due to the influence of wind, waves, currents, uncertain model parameters and related noise, there is a certain deviation between actual model and the theoretically model, and the system noise is cross-correlated. In this case, the traditional wave filtering algorithms based on accurate model and independent Gaussian white noise will have some deviation or even divergence. In this paper, a new wave filtering technology is proposed for ship with model uncertainty and cross-correlated noise interference. Specifically, an improved smooth variable structure filter with cross-correlate noise is proposed. The simulation results illustrate the superior performance of the proposed filtering strategy compared with other existing filter algorithm.

关键词： Uncertain parameters Correlated noise smooth variable structure filter Wave filter

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A smooth variable structure filter FOR STATE ESTIMATION

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CONTROL AND INTELLIGENT SYSTEMS 2007年第4期35卷 386-394页

作者： Wang, S. Habibi, S. Burton, R. College of Engineering University of Saskatchewan Saskatoon SK S7N 5A9 57 Campus Drive Canada Department of Mechanical Engineering McMaster University Hamilton ON L8S 4L7 1280 Main Street West Canada

A new method of filtering strategy, referred to as the smooth variable structure filter (SVSF) is reviewed and applied to the problem of state estimation. The reaching stability of the SVSF (closely related to that of variable structure control) is verified by a mathematical proof. The mathematical foundation behind, and the methodology of SVSF are presented. The robustness of SVSF to system noise, measured noise and parameter variations is verified by a numerical example.

关键词： smooth variable structure filter state estimation inverse function

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Convergence Analysis of smooth variable structure filter for River Flow and Stage Estimation using Lagrangian Sensors

Convergence Analysis of Smooth Variable Structure Filter for...

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Global OCEANS Singapore - U.S. Gulf Coast Conference

作者： Ismail, Z. H. Jalaludin, N. A. Univ Teknol Malaysia UTM Malaysia Japan Int Inst Technol MJIIT Kuala Lumpur Malaysia Univ Teknol Malaysia UTM Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur Malaysia Univ Tun Hussein Onn Malaysia UTHM Fac Elect & Elect Engn FKEE Johor Baharu Malaysia

ISBN: (纸本)9781728154466

State estimation of river system includes the flow and stage estimation and used in the estimation of the final sensor's velocity which is proportional to the river velocity. The estimation process includes the system model and measurements. The measurements can be carried out using Eulerian sensor or Lagrangian sensor. The Lagrangian sensor is considered in this research since it able to observe the overall system compared to the Eulerian sensor that perform measurement in fixed position. Besides that, the estimation can be carried out using DA method such as Extended Kalman filter (EKF) and Particle filter (PF). The smooth variable structure filter (SVSF) is introduced to give better response in terms of error reduction by having convergence rate and the smoothing boundary layer width. The convergence analysis of SVSF shows the effectiveness of the proposed method in terms of mathematical representation. The simulation results proved the SVSF have better response compared to the forward simulation, EKF and PF. This shown by 25.6% for RMSE, 25.92% for SD, 15.34% for MAE and the computational time is reduced by 95.51% if compared with PF.

关键词： state estimation smooth variable structure filter Lagrangian sensor convergence analysis

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STATE ESTIMATION OF A FAULTY ACTUATOR USING THE SECOND-ORDER smooth variable structure filter (THE 2ND-ORDER SVSF) 28

STATE ESTIMATION OF A FAULTY ACTUATOR USING THE SECOND-ORDER...

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IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Afshari, Hamed Al-Ani, Dhafar Habibi, Saeid McMaster Univ Dept Mech Engn Hamilton ON L8S 4L8 Canada

ISBN: (纸本)9781479958290

This paper presents the application of the new developed second-order smooth variable structure filter, 2nd-order SVSF, for fault detection under uncertain conditions. The 2nd-order SVSF is a novel model-based state estimation method formulated in a predictor-corrector form. It produces robust state estimation under uncertain conditions, while decreasing the measurement error and its difference, at the same time. The stability of the 2nd-order SVSF is proven using the Lyapunov's stability criterion with the given bounded noise and modeling uncertainties. The corrective gain of the filter is pushing the measurement error and its difference toward zero. This results in providing higher degrees of accuracy, robustness and smoothness in state estimation under uncertain situations. Due to the robust performance of the 2nd-order SVSF for state estimation, it applies to an experimental setup of an Electro-Hydrostatic Actuator (EHA) for fault detection. Experimentation are performed for the EHA setup under the normal and faulty conditions. For the faulty EHA setup, there exists a major friction condition in the EHA's cylinders. The robustness of the new developed 2nd-order SVSF is then verified by comparing its performance with the state-of-the-art filters including the Kalman filter (KF) and the 1st-order SVSF.

关键词： robust state estimation smooth variable structure filter Lyapunov stability criterion fault detection

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An Adaptive smooth variable structure filter based on the Static Multiple Model Strategy 28

An Adaptive Smooth Variable Structure Filter based on the St...

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

作者： Lee, Andrew Gadsden, S. Andrew Wilkerson, Stephen A. Univ Guelph 50 Stone Rd E Guelph ON N1G 2W1 Canada York Coll Penn York PA 17403 USA

ISBN: (纸本)9781510627024

Estimation theory is an important field in mechanical and electrical engineering, and is comprised of strategies that are used to predict, estimate, or smooth out important system state and parameters. The most popular and well-studied estimation strategy was developed over 60 years ago, and is referred to as the Kalman filter (KF). The KF yields the optimal solution in terms of estimation error for linear, well-known systems. Other variants of the KF have been developed to handle modeling uncertainties, non-Gaussian noise, and nonlinear systems and measurements. Although KF-based methods typically work well, they lack robustness to uncertainties and external disturbances - which are prevalent in signal processing and target tracking problems. The smooth variable structure filter (SVSF) was introduced in an effort to provide a more robust estimation strategy. In an effort to improve the robustness and filtering strategy further, this paper introduces an adaptive form of the SVSF based on the static multiple model strategy.

关键词： smooth variable structure filter Static Multiple Models Estimator Extended Kalman filter

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Application of the smooth variable structure filter to a Multi-Target Tracking Problem

Application of the Smooth Variable Structure Filter to a Mul...

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Conference on Signal Processing, Sensor Fusion, and Target Recognition XX

作者： Gadsden, S. A. Dunne, D. Tharmarasa, R. Habibi, S. R. Kirubarajan, T. McMaster Univ Dept Mech Eng 1280 Main St W Hamilton ON L8S 4L7 Canada McMaster Univ Hamilton ON L8S 4L8 Canada

ISBN: (纸本)9780819486240

The most popular and well-studied estimation method is the Kalman filter (KF), which was introduced in the 1960s. It yields a statistically optimal solution for linear estimation problems. The smooth variable structure filter (SVSF) is a relatively new estimation strategy based on sliding mode theory, and has been shown to be robust to modeling uncertainties. The SVSF makes use of an existence subspace and of a smoothing boundary layer to keep the estimates bounded within a region of the true state trajectory. This article discusses the application of two estimation strategies (the KF and the SVSF) on a multi-target tracking problem.

关键词： Target tracking estimation strategies Kalman filter smooth variable structure filter

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