Development of a Self-Parameter Tuning Based Adaptive Sliding Mode Observer for Input Fault Reconstruction of Longitudinal Autonomous Driving

2020 ◽  
Author(s):  
Tae-Jun Song ◽  
Kwang-Seok Oh ◽  
Jong-Min Lee ◽  
Kyong-Su Yi
Keyword(s):  
Sliding Mode ◽  
Parameter Tuning ◽  
Autonomous Driving ◽  
Sliding Mode Observer ◽  
Recursive Least Squares ◽  
Adaptive Sliding Mode ◽  
Adaptive Parameter ◽  
Fault Reconstruction ◽  
Injection Parameter ◽  
Evaluation Algorithm

Abstract This paper presents an adaptive sliding mode observer for input fault reconstruction of longitudinal autonomous driving. Sliding mode observer is the robust observer against disturbance, which is used to reconstruct the fault and state estimation. In order to design the injection parameter for sliding mode observer, the boundary of errors that include the fault is required. However, it is difficult to expect the fault magnitude for design the injection parameter. The proposed method is to estimate the proportional constant from the relationship between output error and injection parameter based on recursive least squares. Then, it is used to update the adaptive parameter based on MIT rule. The performance evaluation algorithm was conducted in Matlab/Simulink environment using actual longitudinal driving data and 3-dimensions vehicle model with the applied various faults.

Adaptive Sliding Mode Observer-Based Robust Fault Reconstruction for a Helicopter With Actuator Fault

2015 ◽  
Vol 18 (4) ◽  
pp. 1558-1565 ◽  
Author(s):  
Fuyang Chen ◽  
Kangkang Zhang ◽  
Bin Jiang ◽  
Changyun Wen
Keyword(s):  
Sliding Mode ◽  
Sliding Mode Observer ◽  
Actuator Fault ◽  
Adaptive Sliding Mode ◽  
Fault Reconstruction

Dual-sliding mode approach for separated fault detection and tolerant control for functional safety of longitudinal autonomous driving

2020 ◽  
pp. 095440702096262
Author(s):  
Taejun Song ◽  
Jongmin Lee ◽  
Kwangseok Oh ◽  
Kyongsu Yi
Keyword(s):  
Fault Detection ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Linear Quadratic Regulator ◽  
Fault Tolerant Control ◽  
Autonomous Driving ◽  
Sliding Mode Observer ◽  
Functional Safety ◽  
Linear Quadratic ◽  
Fault Reconstruction

This paper describes model-based separated fault detection and fault tolerant control of longitudinal autonomous driving using dual-sliding mode observer for functional safety. Internal and environment sensors such as camera or radar are required to measure the acceleration information of the subject vehicle and the relative distance and velocity information between the preceding and subject vehicles in longitudinal autonomous driving. In order to detect the independent fault of each sensor, a dual-sliding mode observer (SMO) is used for fault reconstruction under the assumption that V2V (Vehicle to Vehicle) communication for vehicle driving state is available. The each SMO reconstructs the expected fault in sensor based on discontinuous injection term used for converging output error to zero. Based on the reconstructed fault by each SMO, faults are detected using threshold approach. When the fault is detected, the reconstructed fault is used for fault tolerant control by subtracting to faulty data. The proposed fault detection (FD) and fault tolerant control (FTC) algorithms were evaluated using actual driving data and a three-dimensional (3D) vehicle model with a linear quadratic regulator for following control. The evaluation results are presented and analyzed with regard to fault reconstruction, detection, and tolerant control in four cases wherein two types of faults were applied.

An Investigation on Steering Sensor Fault Reconstruction of Autonomous Driving for Functional Safety Using Sliding Mode Observer

2020 ◽  
Author(s):  
Kwang-Seok Oh ◽  
Kyong-Su Yi
Keyword(s):  
Sliding Mode ◽  
Longitudinal Velocity ◽  
Autonomous Driving ◽  
Sliding Mode Observer ◽  
Control Input ◽  
Functional Safety ◽  
Sensor Fault ◽  
Steering Angle ◽  
Fault Reconstruction ◽  
The Stability

Abstract This paper investigates on sensor fault reconstruction of sensors used for steering control of autonomous vehicle for functional safety. Sensor information such as steering angle and longitudinal velocity is generally needed for the design of steering feedback control system. If there exists unexpected fault signals in sensors, fatal accident can occur during autonomous driving because controller cannot compute the accurate control input. In this study, the sliding mode observer has been designed for fault reconstruction of steering angle and velocity sensors. In order to design the observer, the bicycle model that represents dynamic relationship between steering angle and velocities such as lateral velocity and yaw rate of vehicle has been used. The stability analysis has been conducted in accordance with velocity of the vehicle. The fault signals in sensors have been reconstructed using the injection term in sliding mode observer with the sliding mode gains designed for the stability. The performance evaluation has been conducted in Matlab/Simulink environment under the curved path tracking scenario.

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