Adaptive Neural Network Based Fault Detection Design for Unmanned Quadrotor under Faults and Cyber Attacks

2017 ◽  
Author(s):  
Alireza Abbaspour ◽  
Michael Sanchez ◽  
Arman Sargolzaei ◽  
Kang Yen ◽  
Nalat Sornkhampan
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Cyber Attacks ◽  
Adaptive Neural Network

scholarly journals Adaptive neural network control for nonlinear cyber-physical systems subject to false data injection attacks with prescribed performance

2021 ◽  
Vol 379 (2207) ◽  
pp. 20200372
Author(s):  
Zhijie Liu ◽  
Jinglei Tang ◽  
Zhijia Zhao ◽  
Shuang Zhang
Keyword(s):  
Neural Network ◽  
Cyber Physical Systems ◽  
Cyber Attacks ◽  
State Variables ◽  
Adaptive Neural Network ◽  
Physical Systems ◽  
False Data Injection ◽  
Injection Attacks ◽  
Adaptive Neural Network Control ◽  
Output Constraints

Cyber-physical systems (CPSs), as emerging products of industry 4.0 , play a key role in the development of intelligent manufacturing. This paper proposes an observer-based adaptive neural network (NN) control for nonlinear strict-feedback CPSs subject to false data injection attacks. Since there may be strict constraints on the state or output signals of nonlinear cyber-physical systems (NCPSs), we propose a time-varying asymmetric barrier Lyapunov function to realize the specific output constraints of NCPSs under cyber-attacks. Besides, since false data injection attacks will corrupt the transmitted state variables, an observer is designed to obtain observations of the exact states, and NN is used to approximate the unknown nonlinearity of NCPSs. With the proposed control strategy, the constraint control problem of NCPSs subject to false data injection attacks is settled. Finally, a numerical simulation example verifies the effectiveness of the proposed controller. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

Fault Detection for Aircraft Fuel System with Neural Network

2020 ◽  
Author(s):  
Nithya Subramanian ◽  
Hongmei He ◽  
Ian Jennions
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Fuel System ◽  
Aircraft Fuel

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

2020 ◽  
Vol 14 (2) ◽  
pp. 205-220
Author(s):  
Yuxiu Jiang ◽  
Xiaohuan Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lower Limit ◽  
Bp Neural Network ◽  
Accelerator Pedal ◽  
Electronic Throttle ◽  
Measured Voltage ◽  
Upper Limit ◽  
Network Fault

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

Sensor Fault Detection and Validation For Chemical Process Using A Neural Network Model

1997 ◽  
Vol 30 (11) ◽  
pp. 561-566 ◽  
Author(s):  
Koji Morinaga ◽  
Michael E. Sugars ◽  
Koji Muteki ◽  
Haruo Takada
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Chemical Process ◽  
Sensor Fault ◽  
Sensor Fault Detection
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