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

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 Robust Model-Based Strategy for Real-Time Fault Detection and Diagnosis in an Electro-Hydraulic Actuator Using Updated Interactive Multiple Model smooth variable structure filter

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME 2023年第10期145卷 101006-101006页

作者： Saeedzadeh, Ahsan Habibi, Saeid Alavi, Marjan Setoodeh, Peyman McMaster Univ Dept Mech Engn Hamilton ON L8P 0A6 Canada W Booth Sch Engn Practice & Technol Hamilton ON L8S 4L7 Canada

In industries where harsh environments and stringent safety requirements are prevalent, the widespread use of applications has made it essential to focus on fault detection and diagnosis (FDD) in hydraulic actuators. To achieve this, model-based FDD techniques are utilized, which employ estimation tools like observers and filters. However, for many applications, particularly in fluid power systems, observability, and parameter uncertainty pose constraints to extracting information and estimating parameters. To address these issues, an efficient form of interactive multiple model (IMM), called updated IMM (UIMM), is applied to an electro-hydraulic actuator (EHA) to detect and isolate persisting friction and leakage faults. UIMM method progresses through a series of models that correspond to the fault condition's progression instead of considering all models at once (as is done in IMM). This reduces the number of models running simultaneously, providing two significant benefits: enhanced computational efficiency and avoidance of the combinatorial explosion. The smooth variable structure filter with variable boundary layer (SVSF-VBL) is used for state and parameter estimation in conjunction with UIMM. SVSF-VBL is a reliable suboptimal estimation method that performs better than the Kalman filter regarding uncertainties related to the system and modeling. The performance of the UIMM method is validated by the simulation of fault conditions for a typical EHA. A fault tolerable control system (FTCS) has been designed to demonstrate the use of the proposed FDD strategy for fault management in a closed-loop system.

关键词： electro-hydraulic actuator fault detection and diagnosis smooth variable structure filter updated interactive multiple model

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AN ADAPTIVE FORMULATION OF THE smooth variable structure filter BASED ON STATIC MULTIPLE MODELS

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INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2023年第10期38卷

作者： Lee, Andrew S. Gadsden, S. Andrew Wilkerson, Stephen A. AlShabi, Mohammad Northrop Grumman Baltimore MD USA McMaster Univ Dept Mech Engn Hamilton ON Canada York Coll Penn York PA USA Univ Sharjah Dept Mech & Nucl Engn Sharjah U Arab Emirates

The Kalman filter (KF) is the most well-known estimation strategy that yields the optimal solution to the linear quadratic estimation problem. The system in such applications shall be well modelled assuming the presence of Gaussian noise. While the KF is effective under the stated conditions, it lacks robustness to other types of disturbances. Therefore, numerous variants of the KF have been developed to accommodate its limitations. The smooth variable structure filter (SVSF) is an alternative solution with improved robustness, especially in the case of modelling uncertainties. It is based on a sliding-mode technique that offers robustness at the cost of optimality. On the other hand, some algorithms and solutions involve with several possible operating modes and generate an estimation based on the output of these models, i.e., the static multiple models (SMMs) that obtain the estimates based on the weighted statistical fusing of the outputs of the models depending on the likelihood of each mode. This paper introduces an adaptive formulation of the SVSF that is reformulated based on SMMs. The proposed model is applied and tested on an electro-hydrostatic actuator (EHA). The proposed method takes the advantages of the SVSF's robustness and stability while reducing the estimation error due to the use of an adaptive modelling structure. The results show an improvement on the SVSF performance, where the root mean-squared errors are reduced by 41%, 99%, and 75% for the position, velocity, and acceleration estimated states. Therefore, the proposed method is a good candidate for parameter and state estimation problems.

关键词： State and parameter estimation Kalman filter smooth variable structure filter robustness static multiple models

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State of Charge Estimation of Lithium-ion Battery in Electric Vehicles Using the smooth variable structure filter: Robustness Evaluation against Noise and Parameters Uncertainties

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第15期51卷 1630-1647页

作者： Ben Lazreg, Meriem Baccouche, Ines Jemmali, Sabeur Manai, Bilal Hamouda, Mahmoud Univ Sousse Ecole Natl Ingn Sousse LATIS Lab Adv Technol & Intelligent Syst Sousse 4023 Tunisia CEGEP Outaouais Dept Elect Engn Gatineau PQ Canada

State of Charge (SoC) of Lithium-ion battery is a key parameter in battery management systems for electric vehicles. This paper uses the fundamental theory of the smooth variable structure filter (SVSF) and proposes a SoC estimation algorithm for a Manganese Cobalt (NMC) cell with a nominal capacity of 20 Ah. Several tests are conducted considering different types of noise and parameters variation. A nonrandom Gaussian noise is first added to the battery voltage. The maximum root mean square error (RMSE) of the estimated SoC is about 2.8% for a standard deviation of the noise set to 2.6e(-3) P.U. The same noise is applied to the battery current and the maximum RMSE of the SoC is obtained as 1.36%. Moreover, an EMI noise is added to the battery voltage and the obtained RMSE of the SoC is about 1.73% for a peak amplitude of the noise set to 0.07 P.U. The convergence of the algorithm is also confirmed under battery parameters variation due to the temperature change. However, its accuracy degrades considerably. Finally, a comparative study is carried out with the extended Kalman filter and shows the superiority of SVSF in terms of accuracy and robustness against measurement noise.

关键词： Li-ion battery state of charge battery management system electric vehicle smooth variable structure filter electromagnetic interference

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A new strategy of smooth variable structure filter to estimate unmeasured states in target tracking

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DIGITAL SIGNAL PROCESSING 2022年第0期123卷 103428-103428页

作者： Chen, Yu Wang, Guangmin Xu, Luping Yan, Bo Xidian Univ Sch Aerosp Sci & Technol Xian 710126 Shaanxi Peoples R China Univ Bologna Dept Elect Elect & Informat Engn I-47521 Cesena FC Italy

In the field of target tracking, the Kalman filter is one of the most popular filters, but its robustness will decrease when target maneuvers. The novel smooth variable structure filter (SVSF) has attracted much attention for its robustness against disturbances and uncertainties. In linear systems, the existing methods of SVSF can estimate unmeasured states, which refer to the states that cannot be measured by sensors, but the estimation is with low accuracy. And there is little research on the estimation for unmeasured states by SVSF in nonlinear systems. Therefore, this paper has put forward a novel strategy to improve the estimation accuracy of unmeasured states in both linear and nonlinear systems. The proposed strategy uses the correlation of error covariance among state elements to calculate the value of unmeasured states. On the basis of the strategy deduced from linear system, one algorithm in linear system and two new extended algorithms in nonlinear systems are proposed. In simulations, different filter models are used by all filters to process Gaussian and non-Gaussian noise in linear and nonlinear systems. The comprehensive results prove that, the proposed linear algorithm has better performance than the existing methods of SVSF in linear systems. Two nonlinear algorithms also show good performance in the estimation for unmeasured states in nonlinear and non-Gaussian systems. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Target tracking smooth variable structure filter Kalman filter Nonlinear and non-Gaussian systems State estimation

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A new particle filter based on smooth variable structure filter

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2020年第1期34卷 32-41页

作者： Zhou, Haomiao Xia, Yuanqing Deng, Yulin Beijing Inst Technol Sch Life Sci Beijing Peoples R China Tangshan Univ Intelligence & Informat Engn Coll Tangshan Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

In this paper, a new particle filter (PF) based on the smooth variable structure filter (SVSF) is presented. Here, PF is a popular estimation method that makes use of distributed particles to form an approximation of the probability distribution function. The SVSF is a sliding mode estimator, which is based on predictor-corrector methods and sliding mode concepts. Moreover, the gain of the SVSF is calculated based on a switching surface. The new filter called SVSF-PF utilizes a smoothing boundary layer of SVSF to control the particles in a range, which can improve the overall estimation accuracy and stability. Finally, a simulation example of nonlinear target tracking is used to demonstrate effectiveness.

关键词： chattering particle filter smooth variable structure filter

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An Improved smooth variable structure filter for Robust Target Tracking

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REMOTE SENSING 2021年第22期13卷 4612-4612页

作者： Chen, Yu Xu, Luping Wang, Guangmin Yan, Bo Sun, Jingrong Xidian Univ Sch Aerosp Sci & Technol Xian 710126 Peoples R China Univ Bologna Dept Elect Elect & Informat Engn I-47521 Cesena FC Italy

As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters.

关键词： state estimation target tracking smooth variable structure filter Kalman filter

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The smooth variable structure filter: A comprehensive review

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DIGITAL SIGNAL PROCESSING 2021年 110卷 102912-102912页

作者： Avzayesh, Mohammad Abdel-Hafez, Mamoun AlShabi, M. Gadsden, S. A. Amer Univ Sharjah Dept Mech Engn POB 26666 Sharjah U Arab Emirates Univ Sharjah Dept Mech & Nucl Engn POB 27272 Sharjah U Arab Emirates Univ Guelph Coll Engn & Phys Sci Guelph ON Canada

The smooth variable structure filter (SVSF) is a type of sliding mode filter formulated in a predictor-corrector format and has seen significant development over the last 15 years. In this paper, we provide a comprehensive review of the SVSF and its variants. The developments, applications and improvements of the SVSF in terms of robustness and optimality are investigated. In addition, the combination of the SVSF with different filtering strategies is considered in an effort to improve estimation accuracy while maintaining robustness to model uncertainty. State estimation techniques such as the unscented and cubature Kalman filters (UKF & CKF), SVSF, the combination of SVSF with UKF (UK-SVSF), and the combination of CKF with SVSF (CK-SVSF) are applied on a 4-DOF industrial robotic arm. The SVSF state estimation performance is examined under different operating conditions. The results of these filters have been compared based a number of statistics such as the root mean squared error (RMSE) and mean absolute error (MAE), among others. It is shown that the UK-SVSF and CK-SVSF strategies acquire the best performance in the presence of uncertainties. (C) 2020 The Authors. Published by Elsevier Inc.

关键词： Estimation theory filtering Kalman filters smooth variable structure filter Robustness

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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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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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