A new adaptive smooth variable structure filter SLAM algorithm for unmanned vehicle

2017 ◽  
Author(s):  
Fethi Demim ◽  
Abdelghani Boucheloukh ◽  
Abdelkrim Nemra ◽  
Kahina Louadj ◽  
Mustapha Hamerlain ◽  
...  
Keyword(s):  
Variable Structure ◽  
Unmanned Vehicle ◽  
Slam Algorithm ◽  
Smooth Variable Structure Filter

scholarly journals An ASVSF-SLAM Algorithm with Time-Varying Noise Statistics Based on MAP Creation and Weighted Exponent

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yingzhong Tian ◽  
Heru Suwoyo ◽  
Wenbin Wang ◽  
Long Li
Keyword(s):  
Continuous System ◽  
Variable Structure ◽  
Measurement Noise ◽  
Time Varying ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Filter Divergence ◽  
Correction Step ◽  
Noise Statistics ◽  
Smooth Variable Structure Filter

The probability-based filtering method has been extensively used for solving the simultaneous localization and mapping (SLAM) problem. Generally, the standard filter utilizes the system model and prior stochastic information to approximate the posterior state. However, in the real-time situation, the noise statistics properties are relatively unknown, and the system is inaccurately modeled. Thus the filter divergence might occur in the integration system. Moreover, the expected accuracy might be challenging to be reached due to the absence of the responsive time-varying of both the process and measurement noise statistic which naturally can enlarge the uncertainty in the continuous system. Consequently, the traditional strategy needs to be improved aiming to provide an ability to estimate those properties. In order to accomplish this issue, the new adaptive filter is proposed in this paper, termed as an adaptive smooth variable structure filter (ASVSF). Sequentially, the improved SVSF is derived and implemented; the process and measurement noise statistics are estimated by utilizing the maximum a posteriori (MAP) creation and the weighted exponent concept, and the covariance correction step is added based on the divergence suppression concept. In this paper the ASVSF is applied to overcome the SLAM problem of an autonomous mobile robot; henceforth it is abbreviated as an ASVSF-SLAM algorithm. It is simulated and compared to the classical algorithm. The simulation results demonstrated that the proposed algorithm has better performance, stability, and effectiveness.

scholarly journals Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8560
Author(s):  
Sara Rahimifard ◽  
Saeid Habibi ◽  
Gillian Goward ◽  
Jimi Tjong
Keyword(s):  
State Estimation ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Variable Structure ◽  
Internal Resistance ◽  
Accurate Estimation ◽  
Battery Management ◽  
Robust Adaptive ◽  
State Of Power ◽  
Smooth Variable Structure Filter

Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery’s SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than 2% over the full operating range of SoC along with an accurate estimation of SoH.

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.

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