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.

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.

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.

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.

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.

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.

Automotive Tracking Technique Using a New IMM Based PDA-SVSF

2014 ◽  
Author(s):  
Mina Attari ◽  
Saeid Habibi
Keyword(s):  
Variable Structure ◽  
Vehicle Tracking ◽  
Vehicle Safety ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Modeling Uncertainties ◽  
Self Driving Cars ◽  
Safety Systems ◽  
Smooth Variable Structure Filter ◽  
Real Experimental Data

Car tracking algorithms are important for a number of applications, including self-driving cars and vehicle safety systems. The probabilistic data association (PDA) algorithm, in conjunction with Kalman Filter (KF), and interacting multiple model (IMM) are well studied, specifically in the aero-tracking applications. This paper studies single targets while performing maneuvers in the presence of clutter, which is a common scenario for road vehicle tracking applications. The relatively new smooth variable structure filter (SVSF) is demonstrated to be robust and stable filtering strategy under the presence of modeling uncertainties. In this paper, SVSF based PDA technique is combined with IMM method. The new method, referred to as IMM-PDA-SVSF is simulated under several possible car motion scenarios. Also, the algorithm is tested on a real experimental data acquired by GPS device.

scholarly journals Adaptive Fuzzy Sliding Mode Guidance Law considering Available Acceleration and Autopilot Dynamics

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yulin Wang ◽  
Shengjing Tang ◽  
Wei Shang ◽  
Jie Guo
Keyword(s):  
Sliding Mode ◽  
Variable Structure ◽  
Adaptive Sliding Mode Control ◽  
Maneuvering Target ◽  
Guidance Law ◽  
Target Acceleration ◽  
Neural Network System ◽  
Fuzzy Sliding Mode ◽  
Miss Distance ◽  
Terminal Guidance

Terminal guidance law for missiles intercepting high maneuvering targets considering the limited available acceleration and autopilot dynamics of interceptor is investigated. Conventional guidance laws based on adaptive sliding mode control theory were designed to intercept a maneuvering target. However, they demand a large acceleration for interceptor at the end of the terminal guidance, which may have acceleration saturation especially when the target acceleration is close to the available acceleration of interceptor. In this paper, a terminal guidance law considering the available acceleration and autopilot dynamics of interceptor is proposed. Then, a fuzzy system is utilized to approximate and replace the variable structure term, which can handle the unknown target acceleration. And an adaptive neural network system is adopted to compensate the effects caused by the designed overlarge acceleration of interceptor such that the interceptor with small available acceleration can intercept the high maneuvering target. Simulation results show that the guidance law with available acceleration and autopilot dynamics (AAADG) is highly effective for reducing the acceleration command and achieving a small final miss distance.

Sign in / Sign up

Export Citation Format

Share Document