A Review of Smooth Variable Structure Filters: Recent Advances in Theory and Applications

2015 ◽  
Author(s):  
S. Andrew Gadsden ◽  
Hamed H. Afshari
Keyword(s):  
Review Paper ◽  
Sliding Mode ◽  
Variable Structure ◽  
Estimation Methods ◽  
Original Form ◽  
Estimation Technique ◽  
Modeling Errors ◽  
Recent Developments ◽  
Predictor Corrector ◽  
Smooth Variable Structure Filter

The smooth variable structure filter (SVSF) is a relatively new state and parameter estimation technique. Introduced in 2007, it is based on the sliding mode concept, and is formulated in a predictor-corrector fashion. The main advantages of the SVSF, over other estimation methods, are robustness to modeling errors and uncertainties, and its ability to detect system changes. Recent developments have looked at improving the SVSF from its original form. This review paper provides an overview of the SVSF, and summarizes the main advances in its theory.

scholarly journals An Improved Smooth Variable Structure Filter for Robust Target Tracking

2021 ◽  
Vol 13 (22) ◽  
pp. 4612
Author(s):  
Yu Chen ◽  
Luping Xu ◽  
Guangmin Wang ◽  
Bo Yan ◽  
Jingrong Sun
Keyword(s):  
Target Tracking ◽  
Sliding Mode ◽  
Sudden Change ◽  
Variable Structure ◽  
The State ◽  
Modeling Uncertainties ◽  
Modeling Errors ◽  
The Face ◽  
Predictor Corrector ◽  
Smooth Variable Structure Filter

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.

Fault Detection of an Electrohydrostatic Actuator With the SVSF Time-Varying Boundary Layer Concept

2013 ◽  
Author(s):  
S. Andrew Gadsden ◽  
Saeid R. Habibi
Keyword(s):  
Boundary Layer ◽  
Sliding Mode ◽  
Variable Structure ◽  
Estimation Methods ◽  
Gear Pump ◽  
Time Varying ◽  
Aerospace Applications ◽  
Performance Metric ◽  
Efficient Type ◽  
Predictor Corrector

The electrohydrostatic actuator (EHA) is an efficient type of actuator commonly used in aerospace applications. It makes use of a closed hydraulic circuit, a number of control valves, an electric motor, and a fluid pump (usually a type of gear pump). The smooth variable structure filter (SVSF) is a relatively new estimation strategy based on sliding mode concepts formulated in a predictor-corrector fashion. The SVSF offers a number of advantages over other traditional estimation methods, including robust and stable estimates, and an additional performance metric. A fixed smoothing boundary layer was implemented in an effort to ensure stable estimates, and is defined based on the amount of uncertainties and noise present in the estimation process. Recent advances in SVSF theory include a time-varying smoothing boundary layer. This method, known as the SVSF-VBL, offers an optimal formulation of the SVSF as well as a method for detecting changes or faults in a system. This paper implements the SVSF-VBL in an effort to detect faults in an EHA. The results are compared with traditional Kalman filter-based methods.

Target Tracking Using the Smooth Variable Structure Filter

2009 ◽  
Author(s):  
Andrew Gadsden ◽  
Saeid Habibi
Keyword(s):  
Target Tracking ◽  
Sliding Mode ◽  
Initial Conditions ◽  
A Priori ◽  
Variable Structure ◽  
Tracking Problem ◽  
Tracking Accuracy ◽  
State Estimate ◽  
Predictor Corrector ◽  
Smooth Variable Structure Filter

This article discusses the application of the smooth variable structure filter (SVSF) on a target tracking problem. The SVSF is a relatively new predictor-corrector method used for state and parameter estimation. It is a sliding mode estimator, where gain switching is used to ensure that the estimates converge to true state values. An internal model of the system, either linear or nonlinear, is used to predict an a priori state estimate. A corrective term is then applied to calculate the a posteriori state estimate, and the estimation process is repeated iteratively. The results of applying this filter on a target tracking problem demonstrate its stability and robustness. Both of these attributes make using the SVSF advantageous over the well-known Kalman and extended Kalman filters. The performances of these algorithms are quantified in terms of robustness, resilience to poor initial conditions and measurement outliers, tracking accuracy and computational complexity.

Condition Monitoring of an Electro-Hydrostatic Actuator Using the Dynamic 2nd-Order Smooth Variable Structure Filter

2015 ◽  
Author(s):  
Hamed Hossein Afshari ◽  
Stephan Andrew Gadsden ◽  
Saeid Habibi
Keyword(s):  
Measurement Error ◽  
State Estimation ◽  
Condition Monitoring ◽  
Sliding Mode ◽  
Variable Structure ◽  
Second Order ◽  
Estimation Methods ◽  
Scenarios Simulation ◽  
Existence Subspace ◽  
Smooth Variable Structure Filter

This paper introduces the dynamic 2nd-order Smooth Variable Structure Filter (Dynamic 2nd-order SVSF) method for the purpose of robust state estimation. Thereafter, it presents an application of this method for condition monitoring of an electro-hydrostatic actuator system. The SVSF-type filtering is in general designed based on the sliding mode theory; whereas the sliding mode variable is equal to the innovation (measurement error). In order to formulate the dynamic 2nd-order SVSF, a dynamic sliding mode manifold is defined such that it preserves the first and second order sliding conditions. This causes that the measurement error and its first difference are pushed toward zero until reaching the existence subspace. Hence, this filter benefits from the robustness and chattering suppression properties of the second order sliding mode systems. These help the filter to suppress the undesirable chattering effects without the need for approximation or interpolation that however reduces accuracy and robustness of the SVSF-type filtering. In order to investigate the performance of the dynamic 2nd-order SVSF for state estimation, it applies to an Electro-Hydrostatic Actuator (EHA) system under the normal and uncertain scenarios. Simulation results are then compared with ones obtained by other estimation methods such as the Kalman filter and the 1st-order SVSF method.

scholarly journals A Novel Smooth Variable Structure Smoother for Robust Estimation

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1781
Author(s):  
Yu Chen ◽  
Luping Xu ◽  
Bo Yan ◽  
Cong Li
Keyword(s):  
Sliding Mode ◽  
Variable Structure ◽  
Estimation Accuracy ◽  
Maneuvering Target Tracking ◽  
State Estimate ◽  
Maneuvering Target ◽  
Modeling Uncertainties ◽  
New Type ◽  
Gaussian System ◽  
Smooth Variable Structure Filter

The smooth variable structure filter (SVSF) is a new-type filter based on the sliding-mode concepts and has good stability and robustness in overcoming the modeling uncertainties and errors. However, SVSF is insufficient to suppress Gaussian noise. A novel smooth variable structure smoother (SVSS) based on SVSF is presented here, which mainly focuses on this drawback and improves the SVSF estimation accuracy of the system. The estimation of the linear Gaussian system state based on SVSS is divided into two steps: Firstly, the SVSF state estimate and covariance are computed during the forward pass in time. Then, the smoothed state estimate is computed during the backward pass by using the innovation of the measured values and covariance estimate matrix. According to the simulation results with respect to the maneuvering target tracking, SVSS has a better performance compared with another smoother based on SVSF and the Kalman smoother in different tracking scenarios. Therefore, the SVSS proposed in this paper could be widely applied in the field of state estimation in dynamic system.

Friction Fault Detection of an Electrohydrostatic Actuator

2011 ◽  
Author(s):  
S. A. Gadsden ◽  
Y. Song ◽  
K. R. McCullough ◽  
S. R. Habibi
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Sliding Mode ◽  
Variable Structure ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Estimation Strategy ◽  
Operating Modes ◽  
Smooth Variable Structure Filter ◽  
Novel Model

This article discusses the application of a novel model-based fault detection method. The method is based on the interacting multiple model (IMM) strategy, which makes use of a finite number of known operating modes. A filter is used in conjunction with the IMM in order to estimate the states and parameters of the system. The smooth variable structure filter (SVSF) is a relatively new estimation strategy, and is based on sliding mode concepts which introduces an inherent amount of robustness and stability. The combined SVSF-IMM strategy is applied on an electrohydrostatic actuator (EHA), which is a device used in the aerospace industry. Two different operating modes were created, based on varying degrees of friction acting on the EHA cylinder. The results of the friction fault detection were compared with the popular Kalman filter (KF) based IMM strategy.

Iterative Smooth Variable Structure Filter for Parameter Estimation

2011 ◽  
Vol 2011 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammad Al-Shabi ◽  
Saeid Habibi
Keyword(s):  
Parameter Estimation ◽  
Sliding Mode ◽  
Estimation Method ◽  
Variable Structure ◽  
Model Parameters ◽  
Modeling Uncertainties ◽  
Control Concept ◽  
Chattering Effect ◽  
Combined Strategy ◽  
Smooth Variable Structure Filter

The smooth variable structure filter (SVSF) is a recently proposed predictor-corrector filter for state and parameter estimation. The SVSF is based on the sliding mode control concept. It defines a hyperplane in terms of the state trajectory and then applies a discontinuous corrective action that forces the estimate to go back and forth across that hyperplane. The SVSF is robust and stable to modeling uncertainties making it suitable for fault detection application. The discontinuous action of the SVSF results in a chattering effect that can be used to correct modeling errors and uncertainties in conjunction with adaptive strategies. In this paper, the SVSF is complemented with a novel parameter estimation technique referred to as the iterative bi-section/shooting method (IBSS). This combined strategy is used for estimating model parameters and states for systems in which only the model structure is known. This combination improves the performance of the SVSF in terms of rate of convergence, robustness, and stability. The benefits of the proposed estimation method are demonstrated by its application to an electrohydrostatic actuator.

scholarly journals Synchronous Generator Rectification System Based on Double Closed-Loop Control of Backstepping and Sliding Mode Variable Structure

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1832
Author(s):  
Jinfeng Liu ◽  
Xin Qu ◽  
Herbert Ho-Ching Iu
Keyword(s):  
Closed Loop ◽  
Control Structure ◽  
Low Voltage ◽  
Sliding Mode ◽  
Variable Structure ◽  
Synchronous Generator ◽  
Closed Loop Control ◽  
High Current ◽  
Loop Control ◽  
Sliding Mode Variable Structure

Low-voltage and high-current direct current (DC) power supplies are essential for aerospace and shipping. However, its robustness and dynamic response need to be optimized further on some special occasions. In this paper, a novel rectification system platform is built with the low-voltage and high-current permanent magnet synchronous generator (PMSG), in which the DC voltage double closed-loop control system is constructed with the backstepping control method and the sliding mode variable structure (SMVS). In the active component control structure of this system, reasonable virtual control variables are set to obtain the overall structural control variable which satisfied the stability requirements of Lyapunov stability theory. Thus, the fast-tracking and the global adjustment of the system are realized and the robustness is improved. Since the reactive component control structure is simple and no subsystem has to be constructed, the SMVS is used to stabilize the system power factor. By building a simulation model and experimental platform of the 5 V/300 A rectification module based on the PMSG, it is verified that the power factor of the system can reach about 98.5%. When the load mutation occurs, the DC output achieves stability again within 0.02 s, and the system fluctuation rate does not exceed 2%.

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