Model based Fault Detection and Robust Steering Control Algorithm for Autonomous Vehicle Using Sliding Mode Observer

2017 ◽  
Vol 25 (6) ◽  
pp. 732-741
Author(s):  
Kwangseok Oh ◽  
Kyongsu Yi
Keyword(s):  
Fault Detection ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
Sliding Mode Observer ◽  
Steering Control ◽  
Model Based

Development of an Adaptive Prediction Time Horizon Based Model Predictive Steering Control Algorithm for Autonomous Vehicle With Disturbance Estimation

2020 ◽  
Author(s):  
Kwang-Seok Oh ◽  
Tae-Jun Song
Keyword(s):  
Time Horizon ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
System Model ◽  
Prediction Algorithm ◽  
Steering Control ◽  
Disturbance Estimation ◽  
Adaptive Prediction ◽  
Prediction Time

Abstract This paper proposes an adaptive prediction time horizon based model predictive steering control algorithm for autonomous vehicle with disturbance estimation. The model predictive control (MPC) requires relatively accurate system model to compute the reasonable control inputs. There exists model uncertainty between system model and actual system. And the uncertainty can be increased in the prediction step of MPC and that can have an influence on control performance. In order to address the issue, the uncertainty has been estimated using the sliding mode observer and predicted in the designed prediction algorithm based on gray prediction model. Using the predicted future uncertainties, the number of prediction steps has been determined with the uncertainty threshold value. The path tracking control algorithm for autonomous driving has been used to compute the steering angle of vehicle. The performance evaluation of the adaptive MPC has been conducted using the simplified bicycle model in Matlab/Simulink environment under the lane change scenario.

scholarly journals Fault detection and isolation for a multi-cellular converter based on sliding mode observer

2015 ◽  
Vol 48 (21) ◽  
pp. 164-170 ◽  
Author(s):  
H. Meziane ◽  
C. Labarre ◽  
S. Lefteriu ◽  
M. Defoort ◽  
M. Djemai
Keyword(s):  
Fault Detection ◽  
Sliding Mode ◽  
Sliding Mode Observer ◽  
Fault Detection And Isolation

scholarly journals Estimation and Fault Diagnosis of Lithium-Ion Batteries: A Fractional-Order System Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shulan Kong ◽  
Mehrdad Saif ◽  
Guozeng Cui
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fractional Order ◽  
Sliding Mode ◽  
Estimation Error ◽  
Lithium Ion ◽  
Fault Isolation ◽  
Sliding Mode Observer ◽  
Fault Detection And Isolation ◽  
Order System

This study investigates estimation and fault diagnosis of fractional-order Lithium-ion battery system. Two simple and common types of observers are designed to address the design of fault diagnosis and estimation for the fractional-order systems. Fractional-order Luenberger observers are employed to generate residuals which are then used to investigate the feasibility of model based fault detection and isolation. Once a fault is detected and isolated, a fractional-order sliding mode observer is constructed to provide an estimate of the isolated fault. The paper presents some theoretical results for designing stable observers and fault estimators. In particular, the notion of stability in the sense of Mittag-Leffler is first introduced to discuss the state estimation error dynamics. Overall, the design of the Luenberger observer as well as the sliding mode observer can accomplish fault detection, fault isolation, and estimation. The effectiveness of the proposed strategy on a three-cell battery string system is demonstrated.

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