State estimation of a faulty actuator using the second-order smooth variable structure filter (The 2&lt;sup&gt;ND&lt;/sup&gt;-order SVSF)

Afshari, Hamed Hossein; Al-Ani, Dhafar; Habibi, Saeid

doi:10.1109/ccece.2015.7129398

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…The SVSF can be applied to both linear systems modeled as (1) or non-linear systems expressed as in (8):…”

Section: The Smooth Variable Structure Filtermentioning

confidence: 99%

“…Some of the early improvements to the SVSF include a covariance derivation 5,6 , an optimal time varying smoothing boundary width 3,4 , and a strategy for better dealing with missing measurements 4 . In addition, the SVSF has been integrated with Interacting Multiple Model adaptive strategies 5 , a second order SVSF formulation has been derived 7,8 , as has seen many other improvements and applications 9,10,11,12,13,14,15,16 .…”

Section: Introductionmentioning

confidence: 99%

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A multiple model adaptive SVSF-KF estimation strategy

Goodman

Wilkerson

Eggleton

et al. 2019

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

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State estimation strategies play a critical role in obtaining accurate information about the state of dynamic systems as they develop. Such information can be important on its own and critical for precise and predictable control of such systems. The Kalman filter (KF) is a classic algorithm and among the most powerful tools in state estimation. The Kalman filter however can be sensitive to modeling uncertainty and sudden changes in system dynamics. The Smooth Variable Structure Filter (SVSF) is a relatively new estimation strategy that operates on variable structure concepts. In general, the SVSF has the advantage that is can be quite robust to modeling uncertainty and sudden fault conditions. Recent advancements to the SVSF, such as the addition of a covariance formulation, and the derivation of a time varying smoothing boundary layer (VBL), have allowed for combined SVSF-KF strategies. In a typical SVSF-KF approach, the VBL is used to detect the presence of a system fault, and switch from the more optimal KF gain to the more robust SVSF gain. While this approach has been proven effective in several cases, there are circumstances where the VBL will fail to indicate the presence of an ongoing fault. A new form of the SVSF-KF is proposed, based on the framework of the Multiple Model Adaptive Estimator.

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“…The SVSF can be applied to both linear systems modeled as (1) or non-linear systems expressed as in (8):…”

Section: The Smooth Variable Structure Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multiple model adaptive SVSF-KF estimation strategy

Goodman

Wilkerson

Eggleton

et al. 2019

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

View full text Add to dashboard Cite

show abstract

“…Due to these two things, defining noise up front and holding it constant across all iterations is no longer recommended. Instead of getting convergence, the application of this conventional method will actually make SVSF provide a state of divergence when applied to real conditions [4,13,14]. Therefore, it is obvious that SVSF needs to be enhanced before it is implemented.…”

Section: Introductionmentioning

confidence: 99%

The use of Fuzzy Logic Controller and Artificial Bee Colony for optimizing adaptive SVSF in robot localization algorithm

Suwoyo,

Hajar,

Indriyanti

et al. 2024

Sinergi

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The objective of solving feature-based localization problems is to estimate the path of the robot referring to a given map. Thus, it is not surprising that robust estimators such as Smooth Variable Structure Filter (SVSF) are often used to handle this problem. Basically, its use is highly dependent on an accurate system model and known statistical noise. Where neither of these are available by definition. Therefore, the conventional way is not recommended and the use of an adaptive filter approach can be involved. Based on this and although only partially, Innovation Adaptive Estimation (IAE) has been considered to have a positive influence on improving the performance of the estimator. But not infrequently the solutions offered by this approach also lead to divergences due to unmapped dynamic conditions. Moreover, in this proposal, IAE is enhanced by applying Artificial Bee Colony-Tuned Fuzzy Logic. The hope is that there is quality control for the process noise covariance Q and R measurements by updating them based on the output of this ABC-Tuned FLC.

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“…They showed that a nonlinear state equation may be expanded using a polynomial function of arbitrary order and developed a second-order state estimation method based on the nonlinear state model with first-order differential equations [18]. Afshari modeled the dynamics of actuation systems using physical methods [19], [20] and designed filters for robust state estimation for actuation systems under normal and faulty cases [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Second-Order Estimation Strategy for Faulty Systems, 1-14.

Afshari¹,

Lee²,

Gadsden³

et al. 2023

International Journal of Robotics and Automation

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This paper introduces a novel second-order state estimation method that is applied to linear systems dealing with modelling uncertainties. This method produces state estimates by decreasing the innovation sequence (measurement error) and its time difference which results in preserving smoothness and stability against modelling uncertainties. This filter is referred to as the second-order filter since it updates state estimates based on values of the measurement error and its incremental change. The corrective gain of this filter is designed based on a time-varying manifold that is a linear combination of the measurement error and its time difference. This manifold introduces a cut-off frequency coefficient into the filter formulation. The optimal version of the dynamic second-order filter is then calculated by finding the optimal value of this coefficient at each time step such that the state error covariance matrix is minimised. It is shown that the corrective gain of the optimal second-order filter collapses to the Kalman filter's gain for a known model with white noise. In order to verify the accuracy of the method, it is implemented on an aerospace electro-hydrostatic actuator setup under the normal and faulty scenarios.

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State estimation of a faulty actuator using the second-order smooth variable structure filter (The 2<sup>ND</sup>-order SVSF)

Cited by 4 publications

References 7 publications

A multiple model adaptive SVSF-KF estimation strategy

A multiple model adaptive SVSF-KF estimation strategy

The use of Fuzzy Logic Controller and Artificial Bee Colony for optimizing adaptive SVSF in robot localization algorithm

A Dynamic Second-Order Estimation Strategy for Faulty Systems, 1-14.

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