Smoothing Boundary Layer Research Articles

Online estimation of state-of-charge inconsistency for lithium-ion battery based on SVSF-VBL

It is of great significance to estimate cell inconsistency for improving the service life and safety performance of the power battery pack. To accurately estimate the state-of-charge (SOC) inconsistency of cells with different performance parameters and working states, a method combining adaptive robust unscented Kalman filter and smooth variable structure filter with time-varying smoothing boundary layer (SVSF-VBL) is proposed. The cell mean-difference model is used to simulate the behavior characteristics of the battery module, including the cell mean model expressed by the dual polarization (DP) model and the cell difference model characterized by the hypothetical Rint model. Firstly, the improved forgetting factor recursive least square is applied to identify parameters of the DP model, and the unscented Kalman filter incorporating robust estimation and adaptive filter tuning is employed to estimate the SOC of the battery module. Then, SVSF-VBL is used to estimate the SOC difference between each cell and module based on the Rint model for improving the estimation accuracy and robustness. In addition, the comprehensive inconsistency of the cells can be captured by the secondary performance indicator inherent in SVSF-VBL, which contributes to the in-depth study of cell inconsistency. Finally, a series of tests are carried out to verify the performance of the proposed method, and the results show that the method can improve the estimation accuracy and convergence performance while effectively suppressing the system disturbance.

A Hybrid SVSF Algorithm for Automotive Radar Tracking

This paper concerns the robust state estimation of automotive radar targets in presence of model uncertainty. Smooth variable structure filter (SVSF) achieves error-bounded estimation for target state, even with an inaccurate description of target kinematic model. However, it suffers the undesired chattering phenomenon especially in case of a high model uncertainty level, and its performance is sensitive to a preset smoothing boundary layer parameter. In this paper, we propose a novel hybrid SVSF algorithm to handle these two problems simultaneously. First, we derive a nonlinear generalized variable smoothing boundary layer (NGVBL) parameter based on the conventional Tanh-SVSF method by minimizing the pseudo posterior estimation error covariance. Then this NGVBL is employed to realize an adaptive two-module switching strategy with respect to the uncertainty level to calculate the correction gain. If the uncertainty level is high, the undesired chattering is effectively suppressed by the standard Tanh-SVSF gain. In case of a low uncertainty level, the NGVBL is utilized to replace the preset smoothing boundary layer parameter and reformulate the correction gain. Furthermore, it is demonstrated that the NGVBL-based gain is quasi-optimal in the mean square error (MSE) sense. Accordingly, this novel NGVBL-based hybrid SVSF (NGVBL-SVSF) algorithm improves the estimation performance by avoiding parameter sensitivity in a low uncertainty level case, and maintains effective chattering suppression and robustness to increasing uncertainties. Simulation and real-world automotive radar data experiment results show that, the proposed NGVBL-SVSF outperforms existing SVSFs and the classical Kalman filter in terms of tracking accuracy and track continuity.

A new particle filter based on smooth variable structure filter

SummaryIn 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.

Adaptive Predictive Variable Structure Filter for Attitude Synchronization Estimation

In this paper, a novel Predictive Variable Structure Filter (PVSF) and its adaptive deformation (APVSF) are presented for attitude synchronisation during Satellite Formation Flying (SFF). The PVSF is proposed based on the variable structure control concept and applied to any nonlinear system with model errors. The model errors in the PVSF need not satisfy the assumption of Gaussian white noise; therefore, it has advantages in dealing with various kinds of uncertainties, parameter variations or noises. Then, the APVSF is also presented to adjust the smoothing boundary layer of PVSF by minimising the Mean-Square Error (MSE). Simulations are performed to demonstrate the accuracy, robustness, and stability of the proposed methodologies for the attitude synchronisation estimation of the SFF system.

An SVSF-Based Generalized Robust Strategy for Target Tracking in Clutter

Autonomous self-drive requires intelligence and cognition that relies on observations and tracking of the state of motion of surrounding vehicles. This information can be acquired by using sensors; however, these are often affected by clutter and noise that, in turn, introduce the issues of estimation and data origin uncertainty into the tracking system. The most popular methods for estimation and tracking are based on the well-studied Kalman filter (KF). KF is optimal when noise is white and remains so despite uncertainties in the filter model; the robustness and stability of the KF is affected if this condition is not met. The smooth variable structure filter (SVSF) is a relatively new method that is more robust to disturbances and uncertainties. The SVSF ensures stability by using a discontinuous corrective term that maintains estimates to within a subspace of the true state trajectory. The discontinuous corrective term results in chattering that is removed by using a smoothing boundary layer. In this paper, a generalized covariance formulation of the SVSF and a generalized optimal time-varying smoothing boundary layer are proposed. The generalized optimal SVSF is then combined with a joint probabilistic data association technique for target tracking. The robustness and accuracy of the new form of filtering and data association is validated and comparatively analyzed by its application to an experimental traffic monitoring system based on Light Detection and Ranging.

Novel Model-Based Estimators for the Purposes of Fault Detection and Diagnosis

The interacting multiple model (IMM) strategy is particularly useful for systems that behave according to a number of different operating modes. In this strategy, each operating mode is described by a model and has its own filter. The filters are run in parallel, and an overall operating mode probability is calculated that provides an indication of the current operating regime of the system. The smooth variable structure filter (SVSF) is a relatively new estimation method based on the sliding mode concept, formulated in a predictor-corrector form. For systems with modeling uncertainties, the SVSF has shown to be more accurate and robust when compared with other methods such as the extended Kalman filter (EKF). A newer form of the SVSF makes use of a time-varying smoothing boundary layer (SVSF-VBL). This paper introduces new model-based estimators; based on the IMM strategy combined with the SVSF and SVSF-VBL, referred to as the IMM-SVSF and IMM-SVSF-VBL, respectively. The new strategies are applied to a type of aerospace actuator referred to as an electrohydrostatic actuator, which provides a comprehensive system for fault detection and diagnosis. The results are compared with the popular IMM-EKF strategy.

A New Robust Filtering Strategy for Linear Systems

Abstract For linear and well-defined estimation problems with Gaussian white noise, the Kalman filter (KF) yields the best result in terms of estimation accuracy. However, the KF performance degrades and can fail in cases involving large uncertainties such as modeling errors in the estimation process. The smooth variable structure filter (SVSF) is a relatively new estimation strategy based on sliding mode theory and has been shown to be robust to modeling uncertainties. The SVSF makes use of an existence subspace and of a smoothing boundary layer to keep the estimates bounded within a region of the true state trajectory. Currently, the width of the smoothing boundary layer is chosen based on designer knowledge of the upper bound of modeling uncertainties, such as maximum noise levels and parametric errors. This is a conservative choice, as a more well-defined smoothing boundary layer will yield more accurate results. In this paper, the state error covariance matrix of the SVSF is used for the derivation of an optimal time-varying smoothing boundary layer. The robustness and accuracy of the new form of the SVSF was validated and compared with the KF and the standard SVSF by testing it on a linear electrohydrostatic actuator (EHA).