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.

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.

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 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.

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.

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.

Sliding Mode Controller and Filter Applied to a Pneumatic McKibben Muscle Actuator

2009 ◽  
Author(s):  
V. Jouppila ◽  
S. A. Gadsden ◽  
S. R. Habibi ◽  
G. M. Bone ◽  
A. Ellman
Keyword(s):  
Control Strategy ◽  
Sliding Mode ◽  
Variable Structure ◽  
Fast Response ◽  
Sliding Mode Controller ◽  
Modeling Uncertainties ◽  
Full State ◽  
Full State Feedback ◽  
New Strategy ◽  
Mckibben Muscle

In this paper, a robust and stable control strategy is applied to a Festo fluidic muscle actuator, with the objective of trajectory following control. A complete model of this system is not available which leads to unmodeled dynamics and uncertainties. Furthermore, full-state feedback is required for this type of control. However, in practice not all of the states are measurable or available due to cost or availability of instruments, thus a full-state observer is required. The Smooth Variable Structure Filter (SVSF) is a recently introduced robust predictor-corrector method used for state and parameter estimation, and has a form that is able to provide full-state information. In this regard, a new strategy that combines Sliding Mode Control (SMC) with the SVSF is used to control this system. The estimated states from the SVSF are used by the sliding mode controller to obtain a discontinuous control signal. This signal drives the plant to follow a desired state trajectory required by the pneumatic McKibben muscle actuator. Simulation results were generated based on a realistic desired trajectory. The results of the SMC-SVSF control strategy are compared with a tuned PID controller. The described control strategy is able to overcome the nonlinearities present in the system, has a fast response time, and is robust to modeling uncertainties and measurement noise.

PERFORMANCE MEASURES OF THE VARIABLE STRUCTURE FILTER

2005 ◽  
Vol 29 (2) ◽  
pp. 267-295 ◽  
Author(s):  
Saeid Habibi
Keyword(s):  
Performance Measures ◽  
Internal Model ◽  
Variable Structure ◽  
Simulation Studies ◽  
Initial Estimate ◽  
Modeling Uncertainties ◽  
Corrective Term ◽  
System Output ◽  
Predictor Corrector ◽  
The Impact

A new method for state estimation, referred to as the Variable Structure Filter (VSF), has recently been proposed. The VSF is a model based predictor-corrector method. It uses an internal model to provide an initial estimate of the states and subsequently refines this initial estimate by a corrective term that is a function of the system output and the upper bound of uncertainties. As such, the VSF can explicitly cater for uncertainties in its internal model. In this paper, a conceptual discussion of the VSF strategy and its performance in terms of stability, accuracy, and convergence is provided. The impact of modeling uncertainties on the performance of the VSF is discussed and quantified. The analysis is augmented by comparative simulation studies to further illustrate the concept.

Self-Tuning Fuzzy Sliding Controller for the Ship Heading Problem

2010 ◽  
Author(s):  
Nassim Khaled ◽  
Nabil G. Chalhoub
Keyword(s):  
Fuzzy Logic Controller ◽  
Sliding Mode ◽  
Variable Structure ◽  
Guidance System ◽  
Tuning Parameter ◽  
Variable Structure Systems ◽  
Modeling Uncertainties ◽  
Self Tuning ◽  
The Stability ◽  
Yaw Angle

A self-tuning fuzzy-sliding mode controller is presented in the current work. It aims at combining the advantages of the variable structure systems (VSS) theory with the self-tuning fuzzy logic controller. Neither the development of an accurate dynamic model of the plant nor the construction of a rule-based expert system is required for designing the controller. The only requirement is that the upper bound of the modeling uncertainties has to be known. The stability of the controlled system is ensured by forcing the tuning parameter to satisfy the sliding condition. The controller is implemented to control the heading of an under-actuated ship. The simulation results demonstrate the robust performance of the controller in accurately tracking the desired yaw angle specified by the guidance system in the presence of considerable modeling imprecision and environmental disturbances.

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