# FT-BarrierCertificate
UAV Simulation of our paper 'Barrier Certificate based Safe Control for LiDAR-based Systems under Sensor Faults and Attacks'
> H. Zhang, S. Cheng, L. Niu and A. Clark, "Barrier Certificate based Safe Control for LiDAR-based Systems under Sensor Faults and Attacks," 2022 IEEE 61st Conference on Decision and Control (CDC), Cancun, Mexico, 2022, pp. 2256-2263, doi: 10.1109/CDC51059.2022.9992432.
<img src="Figures/image.png" alt="image" height="200" /> <img src="Figures/lidar.png" alt="lidar" height="200" />
## Description
This section evaluates our proposed approach on a UAV delivery system in an urban environment.
The UAV system is based on MATLAB UAV Package Delivery Exampl. The UAV adopts stability, velocity and altitude control modules, rendering its position control dynamics to be:
```math
\begin{equation}
\begin{bmatrix}
[x]_1\\
[x]_2
\end{bmatrix}_{k+1}
=
\begin{bmatrix}
1 & -4.29\times 10^{-5}\\
-1.47\times 10^{-5} & 1
\end{bmatrix}
\begin{bmatrix}
[x]_1\\
[x]_2
\end{bmatrix}_{k}
+
\begin{bmatrix}
0.0019 & -1.93\times 10^{-5}\\
-2.91\times 10^{-4} & 0.0028
\end{bmatrix}
\begin{bmatrix}
[u]_1\\
[u]_2
\end{bmatrix}_{k},
\end{equation}
```
where $x[k]=[[x]_1,[x]_2]^T$ is the UAV position, $[x]_1$ and $[x]_2$ represent the position of UAV on $X$-axis and $Y$-axis, respectively. The UAV has one LiDAR sensor and two inertial navigation system (INS) sensors, denoted as INS1 and INS2. The UAV maintains two EKFs associated with each INS sensor to estimate its position at each time $k$, denoted as $\hat{x}_1[k]$ and $\hat{x}_2[k]$, respectively. The system operates in the presence of an adversary who can compromise one of the INS sensors and spoof the LiDAR sensor.
Fault tolerant estimation for LiDAR-based system removes conflicting state estimations by comparing estimations of proprioceptive sensors with additional information from exteroceptive sensors measurements.
<img src="Figures/FT-Est5.png" alt="FT-Est5" height="200" />
## Getting Started
### Environment
* MATLAB 2020b
* UAV Toolbox for 2020b and its dependency
#### Run FT-LiDAR Estimation
* In UAV_FT-LiDAR_Est directory
* Run *attack_figure.m*
#### Run Baseline under FDI Attacks
* In UAV_Baseline directory
* Open project file *uavPackageDelivery.prj*
* Run file *FlyFullMission.m* or Click the icon indexed 6
* Run Simulation file *uavPackageDelivery.slx* by clicking *RUN*
#### Run FT-Barrier Certificate under FDI Attacks
* In UAV_FTBC directory
* Open project file *uavPackageDelivery.prj*
* Run file *FlyFullMission.m* or Click the icon indexed 6
* Load *mapdataDemo.mat*
* Run Simulation file *uavPackageDelivery.slx* by clicking *RUN*
#### Data and Plots in Our Paper
* In DataFigures directory
* Run *CDC_figure_1.m* to plot FT-LiDAR Estimation figures
* Run *CDC_figure_2.m* to visualize trajectories of the UAV when controlled using our proposed approach and the baseline.
## Code Authors
* Hongchao Zhang, Ph.D. Candidate, hongchao@wustl.edu, \
Electrical & System Engineering, Washington University in St. Louis
* Shiyu Cheng, Ph.D. Student, cheng.shiyu@wustl.edu \
Electrical & System Engineering, Washington University in St. Louis
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传感器故障和攻击下基于 LiDAR 的系统的无人机的安全控制附matlab代码.rar
共339个文件
xml:170个
m:71个
mat:49个
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传感器故障和攻击下基于 LiDAR 的系统的无人机的安全控制附matlab代码.rar (339个子文件)
attack_figure.asv 8KB
uavScenarioBlock.asv 5KB
score_test.asv 5KB
kickLidar_pcdata.asv 3KB
makeDecisionDemo.asv 2KB
offline_xi.asv 518B
compute_extended_matrix.asv 190B
untitled.slx.autosave 24KB
.DS_Store 6KB
.DS_Store 6KB
.DS_Store 6KB
map.fig 29KB
LICENSE 1KB
CDC_figure_1.m 12KB
attack_figure.m 9KB
exampleHelperMAVMissionProtocol.m 8KB
exampleHelperMAVMissionProtocol.m 8KB
MAVLinkInterface.m 7KB
MAVLinkInterface.m 7KB
positon.m 7KB
advPlot.m 6KB
uavScenarioBlock.m 5KB
CDC_figure_2.m 5KB
score_test.m 5KB
attackDemo.m 5KB
attackDemo2.m 5KB
attackDemo2.m 5KB
kickLidar_pcdata.m 3KB
kickLidar_pcdata.m 3KB
exampleHelperMAVMissionTable.m 3KB
exampleHelperMAVMissionTable.m 3KB
test_fmincon.m 3KB
kickLidar.m 3KB
kickLidar.m 2KB
kickLidar.m 2KB
makeDecisionDemo.m 2KB
kickLidar.m 2KB
getQGCPlanFromUAVMissionStruct.m 2KB
getQGCPlanFromUAVMissionStruct.m 2KB
uavScenarioBlock.m 2KB
makeDecisionDemo.m 2KB
makeDecisionDemo.m 2KB
getUAVMissionStructFromQGCPlan.m 1KB
getUAVMissionStructFromQGCPlan.m 1KB
pcEst.m 1KB
pcEst.m 1KB
pcEst.m 1KB
ft_estimator.m 1KB
adversaryDemo.m 1KB
adversaryDemo.m 1KB
adversaryDemo.m 1KB
uav_offline.m 1KB
step_8_highFidelityPlant.m 982B
step_4_obstacleAvoidance.m 971B
step_7_fullObsAvoidance.m 968B
step_5_photorealisticSimulation.m 959B
step_6_fullMission.m 929B
step_2_connectingToQGC.m 899B
step_1_gettingStarted.m 875B
step_5_photorealisticSimulation.m 837B
step_4_obstacleAvoidance.m 834B
step_3_cuboidEnvironment.m 833B
step_6_fullMission.m 830B
step_8_highFidelityPlant.m 829B
step_7_fullObsAvoidance.m 829B
CDC_baseline_attack_time.m 792B
step_2_connectingToQGC.m 792B
baseline_trj.m 755B
CDC_ft_attack_time.m 738B
step_1_gettingStarted.m 729B
step_3_cuboidEnvironment.m 721B
modelInit.m 701B
modelInit.m 662B
clean_up_project.m 646B
clean_up_project.m 646B
set_up_project.m 584B
insTest.m 531B
compare.m 524B
set_up_project.m 521B
quadconstr.m 332B
ft_trj.m 313B
compute_extended_matrix.m 231B
quadhess.m 160B
quadobj.m 104B
UAVTRJ_ft_noekf_all_v1.mat 41.74MB
pointCloud.mat 29.99MB
pointCloud.mat 29.99MB
pointCloud_ins2.mat 29.78MB
pointCloud_ins2.mat 29.78MB
pointCloud_ins2.mat 29.78MB
baseline_scorelog_1800.mat 27.76MB
UAV.mat 14.06MB
uavtrj_ft_noekf_xyz_v1.mat 249KB
uavtrj_ft_noekf_xyz_v1.mat 249KB
uavtrj_ft_noekf_xyz_v1.mat 249KB
uavState.mat 231KB
uavState.mat 231KB
uavState.mat 231KB
FT_bias20_v1.mat 174KB
baseline_trj_2.mat 123KB
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